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GAME’s ON: Unlocking Gaming Analytics: How Wren AI Transforms Backend Data Into Actionable Insights

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How Wren AI Turns Natural-Language Questions Into Cost-Efficient SQL to Accelerate Insights from Gaming Data

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Urgent data requests are inevitable, but Wren AI can bring peace of mind to your team.

The Wren AI Blog

#### No complex schemas. No SQL. Just your data - and instant answers!  From Code Blocks to Conversations

I recently came across a rich synthetic dataset from Kaggle that simulates the booking and review data of a global hotel platform.

![Image_2.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_2_0e322e013d.png)

[https://www.kaggle.com/datasets/alperenmyung/international-hotel-booking-analytics?resource=download ](https://www.kaggle.com/datasets/alperenmyung/international-hotel-booking-analytics)

When I first saw this Kaggle notebook, I instantly recognized the familiar rhythm of a data analyst's workflow:
Setting up the environment, Importing libraries, and preparing to explore a dataset through code. 
![Courtesy of Sonawane Lalit](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_3_a5e0f1fb60.png)

Instead of firing up Jupyter and writing code, I turned to Wren AI - an conversational BI platform that lets you do the same analysis with plain language. 

It's a perfect sandbox for learning - a dataset that's synthetic yet structured enough to simulate real business intelligence and customer satisfaction modeling.

## 🗣️ The Wren AI Approach - Ask, Don't Code
I wanted to try something faster by uploading the CSVs directly into Wren AI and exploring insights instantly through conversational analytics.
1. Go to getwren.ai.
2. Upload the files: hotels.csv, users.csv, and reviews.csv.
3. Wren automatically detects relationships between tables (hotel_id, user_id)

![Image_4.gif](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_4_a896006583.gif)

Within seconds, you'll see your dataset appear as an integrated workspace ready for analysis, instead of writing pd.merge() or groupby, I can just ask questions like:

> "Top 10 cities have the highest review scores?"
![Image_5.gif](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_5_ea2706b071.gif)

Ask follow-up questions like,
> "Compare review scores by traveler type."
![Image_6.gif](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_6_11650aa56d.gif)

The chart showed that solo & business travelers consistently give lower scores, while couples and families tend to rate their stays higher.

> "Break that down by traveler type"
![Image_7.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_7_6383471546.png)

or 
> "Show top 10 hotels in Asia."
![Image_9.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_9_d93477bfbc.png)

## 🔍 Why Wren AI Changes the Game
Traditional BI requires setting up schemas, joins, and dashboards. Wren AI translates each question into optimized SQL, executes it, and visualizes the results - all in one conversational flow. What makes this dataset exciting is how relational it is - the three tables connect naturally, allowing for deeper, multi-angle exploration.

> "Do 5-star hotels outperform 4-star ones?"
![Image_10.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_10_d9cefed3c6.png)

> "Which traveler type gives the lowest ratings?"
![Image_11.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_11_0747ed36f9.png)

> "Which hotels are underperforming based on their star rating?"
![Image_13.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_13_191e0817b0.png)

## 📊 Insights I Discovered
Correlations among review metrics (cleanliness, comfort, staff, location) revealed that staff quality and cleanliness were the strongest predictors of a high overall score - stronger than location or even price.

![Image-14.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_14_4edf9400a1.png)

"Generate reusable Insight or visualizations directly for presentation or reporting."
![Image-15.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_15_fdee4dd974.png)

"Show me average review score and number of reviews per hotel."
![Image_16.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_16_6e6766da4e.png)

"How does traveler age impact hotel choice (based on star rating)Score_staff vs. Score_cleanliness."
![Image_17.png](https://capable-butterfly-84cab64826.media.strapiapp.com/Image_17_3f8e025aa2.png)

It's like having a data analyst that already understands your dataset - and speaks fluent SQL behind the scenes.

## ⚙️ Under the Hood - Wren's Semantic Layer
Behind every question, Wren AI automatically:
1. Maps entities like hotel, user, and review into structured relationships.
2. Understands metrics (scores) vs. dimensions (city, traveler type).
3. Generates the necessary SQL logic on demand.

It's essentially the same intelligence a data analyst applies manually - but expressed through natural language.

Wren AI doesn't replace Python notebooks - it democratizes them. Data analysts still benefit from full control when needed, but business users, marketers, or product leads can now explore the same dataset without technical barriers. 

It bridges the worlds of analytics coding and AI-assisted BI - turning complex data into approachable insights.

## 🌟 Final Thoughts
From Raw Data to Ready Insights - In Minutes
Whether you're in hospitality marketing, hotel management, or guest experience for international brands, Wren AI transforms CSV uploads into business intelligence - supporting decision-making. Ready to turn hotel reviews into competitive insights?

### 👉 Start a free demo at getwren.ai and see actionable data in minutes.

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Conversational Analytics on Hotel Data-Turning 50K Reviews into Insights with Wren AI


Picture this: A 52-year-old patient visits her doctor for a routine check-up. On the surface, everything seems normal: no major complaints, just the everyday stress of balancing work and family. But the data reveals a different story. Her cholesterol has been rising for years, her blood pressure is slightly above average, and she has a family history of stroke.

![68db70236eaa82ca080fc6e5_cover.webp](https://capable-butterfly-84cab64826.media.strapiapp.com/68db70236eaa82ca080fc6e5_cover_354766f199.webp)

This is where Wren AI can change the outcome. Instead of waiting for a crisis, Wren AI enables clinicians to spot risks early and intervene before a medical emergency occurs.

## Why Healthcare Needs Wren AI

Healthcare is one of the most data-intensive industries in the world. Every lab test, hospital visit, prescription, and lifestyle detail generates valuable information. Yet most of this data goes underutilized because:

- **It’s often fragmented across multiple systems.**
- **Physicians don’t have the bandwidth to analyze thousands of variables per patient.**
- **Risk models rely too heavily on population averages instead of personalized data.**

As a result, opportunities for prevention are missed.

## How Wren AI Helps

Wren AI can address these exact challenges by making patient risk analysis more precise, contextual, and actionable:

- **Unified Patient Profiles:** Integrates medical history, lab results, and lifestyle factors into one comprehensive view.
- **Deep Context with MDL:** Using Modeling Definition Language (MDL), Wren AI applies clinically relevant logic so queries reflect real-world healthcare scenarios.

### Knowledge — Instruction

![68db6b060984651084f3c2c3_1_V6pnenJym3GdCrUKsq74Pw.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2c3_1_V6pnen_Jym3_Gd_Cr_U_Ksq74_Pw_2b2d4b3cc6.png)

### Knowledge — question-SQL pairs
- **Pattern Detection:** Finds hidden risk signals, such as the combined effects of smoking, moderate hypertension, and age.
- **Real-Time Insights:** Provides clinicians with instant, data-backed recommendations, saving hours of manual analysis.
- **Empowered Teams:** Even non-technical staff can explore patient data through conversational analytics, supporting population health management, triage, and resource planning with continuously updated dashboards.

## Real Scenarios: Wren AI in Patient Risk Analysis

1. **Analyze Age-Specific Risk**

A care team asks Wren AI:

*“What is the diabetes prevalence by age bracket in our database?”*

![68db6b060984651084f3c2cf_1_EDpQFnmHV7tJ_s7sQWx9Hw.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2cf_1_E_Dp_Q_Fnm_HV_7t_J_s7s_Q_Wx9_Hw_23fe209611.png)

Within seconds, Wren AI identifies the highest-risk groups and generates charts ready for reports.

![68db6b060984651084f3c2c6_1_dMnJfjXEqJqirP3fdan5Mw.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2c6_1_d_Mn_Jfj_X_Eq_Jqir_P3fdan5_Mw_b44e9e11b3.png)

To go deeper, they ask:

*“Which other factors, like BMI, income level, or general health, most strongly predict diabetes risk among different age groups?”*

![68db6b060984651084f3c2d2_1_DoBLBgKyT78Y1k7_9n5kUA.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2d2_1_Do_BL_Bg_Ky_T78_Y1k7_9n5k_UA_776736bbfa.png)

With this insight, clinics can design precise prevention programs rather than relying on broad, one-size-fits-all campaigns.

![68db6b060984651084f3c2bd_1_zlfrRNSqZw-ey9KxFxSXDA.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2bd_1_zlfr_RN_Sq_Zw_ey9_Kx_Fx_SXDA_1096dcca8d.png)

**2\. Quantifying Smoking’s Impact on Stroke Risk**

A clinician types:

*“Compare stroke rates between smokers and non-smokers, controlling for age and hypertension.”*

![68db6b060984651084f3c2cc_1_Bbtpzdoi1OJGugijcbI7FA.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2cc_1_Bbtpzdoi1_OJ_Gugijcb_I7_FA_cec2828fb2.png)

Wren AI instantly returns visualizations and a summary.

![68db6b060984651084f3c2c9_1_ZjTie5MXTG6azWu783jzMw.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2c9_1_Zj_Tie5_MXTG_6az_Wu783jz_Mw_3f174f3a0d.png)

For an even more actionable view, the clinician asks:

*“What is the predicted effect of smoking cessation on stroke risk over the next five years for patients with high cholesterol and hypertension?”*

![68db6b060984651084f3c2d7_1_3IOs8r4iFZYOq04JLfMztQ.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2d7_1_3_I_Os8r4i_FZY_Oq04_J_Lf_Mzt_Q_3e3dae9d93.png)

![68db6b060984651084f3c2c0_1_3vPSsC83ItzTRVER_ig7fw.png](https://capable-butterfly-84cab64826.media.strapiapp.com/68db6b060984651084f3c2c0_1_3v_P_Ss_C83_Itz_TRVER_ig7fw_d9694a1baa.png)

Due to data limitations, Wren AI is unable to provide time-series predictions. However, the chart indicates that individuals in the middle-aged group who quit smoking show a lower risk of stroke. This suggests that smoking cessation can significantly reduce stroke incidence, offering physicians compelling evidence to encourage positive behavioral change.

## Conclusion

AI won’t replace physicians. It amplifies their expertise. By transforming complex data into predictive insights, Wren AI enables healthcare to shift from a reactive to a proactive approach. In healthcare, the real breakthrough isn’t treatment. It’s anticipating and preventing illness.

Prevention is the new cure.

**Ready to transform how your organization accesses data?** [**Request a demo or start your free trial at getwren.ai**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=ads)

‍

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Stop Waiting for Reports: Get Ad-Hoc Patient Insights Instantly with WrenAI

If you’ve worked in structured finance, dashboards are your lifeline.

> *Structured finance provides a strategic solution for large companies with intricate financing requirements that typical loans and mortgages cannot address. — Define at* [*Investopedia*](https://www.investopedia.com/terms/s/structuredfinance.asp)

They track deal waterfalls, monitor delinquency roll rates, and manage collateralized loan facilities, offering a window into performance. Yet, for all their power, dashboards fall short of true self-service. They show what’s happening but leave you to figure out what it means and what to do next. In structured finance, where timing can make or break outcomes, visibility without action isn’t enough.

This is the use case of how financial platforms evolved from manual processes to self-service systems that turn data into decisions — with **Wren AI at the center of this transformation**.

## Solving the Analytics Ecosystem’s Pain Points

Data teams juggle warehouses (like [Snowflake](https://getwren.ai/post/how-snowflake-building-the-most-powerful-sql-llm-in-the-world), AWS, GCP..), BI tools (such as Qlik, Tableau, Looker, Metabase), spreadsheets, and messaging apps. Insights get delayed, opportunities slip away, and analysts become bottlenecks.

Wren AI integrates with [Snowflake](https://docs.getwren.ai/cloud/guide/connect/snowflake) by connecting through the Snowflake database’s outbound IP, which must be added to Snowflake’s firewall. Users can set up the connection via Wren AI’s interface by providing the account name, database, schema, warehouse, and authentication details (username/password or key pair). Once connected, Wren AI’s text2SQL capabilities allow users to query Snowflake’s data warehouse using natural language, which is converted into SQL for execution.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55efd_0*e4czWxdKEoErb6TU.png)


This enables efficient data exploration, visualization, and modeling, leveraging Snowflake’s scalable cloud infrastructure for real-time analytics and insights. Here’s how Wren AI tackles these challenges:

1. **Fragmented Workflows**: Wren AI embeds analytics into [Slack](https://docs.getwren.ai/cloud/guide/slack), Teams, or your portal, so insights flow seamlessly within your existing tools. No more platform-hopping.
2. **Analyst Bottleneck**: With natural language queries, anyone can get answers without waiting for an analyst to write SQL or tweak a dashboard. Analysts are freed to focus on strategy, not firefighting.
3. **Static Dashboards, Dynamic Needs**: Wren AI handles evolving questions like “What’s my UPB trend by facility?” or “What if defaults rise 10% in Texas?” — delivering real-time visualizations and simulations.
4. **Adoption Gaps**: Most non-technical users avoid BI tools. Wren AI meets them where they work, boosting adoption across teams.

## Why Wren AI Stands Out

- **Natural Language Power**: Ask, “Show me a roll-rate matrix for the last 12 months” or “What’s my CA exposure by county?” and get instant, tailored visualizations.
- **Scalable Deployment**: Choose a cloud model with transparent credit pricing or a self-hosted option with seat-based licensing and 150,000 [monthly API calls](https://docs.getwren.ai/cloud/guide/api-access/overview) — perfect for compliance-conscious enterprises.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55f07_1*xDUF3FE2UWqWem8rw17FOA.png)
[https://huggingface.co/spaces/getWrenAI/wrenai-cloud-api-demo](https://huggingface.co/spaces/getWrenAI/wrenai-cloud-api-demo)

- **Democratized Insights**: Empower everyone — risk managers, executives, and analysts — to self-serve insights, freeing data teams for high-value work.

## Wren AI: The Self-Service GenBI

Wren AI is at the core of transforming finance, designed to turn raw data into actionable insights. As the GenBI platform in advanced portfolio management systems, it delivers reliable results to drive smarter decisions.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55f00_1*iIyy0i4XIFWYYY1x4vN-lQ.png)

- **Translating Queries**: Converts natural-language requests into precise SQL, enabling self-service analytics for everyone, not just SQL-savvy analysts.
- **Preparing Data**: Cleans, filters, and standardizes inputs so every downstream agent — risk, sentiment, trade execution — works with analysis-ready datasets.
- **Generating Insights**: Through Generative BI, Wren AI produces instant charts, summaries, and reports, streamlining analyst workflows.
- **Collaborating Across Agents**: Acts as the shared data layer in multi-agent systems, empowering specialized agents to focus on forecasting, risk modeling, or execution.

In other words, **Wren AI is the “data operating system” of agentic finance** — providing the semantic consistency, governance, and accuracy that turn self-service from theory into practice.

## Overcoming Compliance Challenges with Wren AI

The move to self-service isn’t without challenges. Regulatory compliance, data privacy, and legacy integration are real hurdles. GDPR and SEC rules demand rigorous data handling, and older platforms often resist AI-driven change.

- **Compliance-first semantics** that keep definitions consistent across platforms.
- **Flexible APIs** that integrate smoothly with legacy warehouses and BI tools.
- **Governed self-service** that democratizes insights without sacrificing controls through **role-based access control (RBAC)** and privileged project sharing.

This ensures structured finance firms can adopt agentic AI safely, securely, and without ripping out existing infrastructure.

## The Breakthrough: What Wren AI Brings

Wren AI’s value proposition is not to replace existing BI tool, but to act as a conversational, intelligent layer on top of the existing **underlying infrastructure**. It transforms the user experience from point-and-click to conversation.

### 1\. The Conversational Interface for Data

- **Instant, Ad-Hoc Answers:** Instead of an analyst navigating through dashboards or building a new query, they can simply ask a question in plain English. For example:

*Plot a histogram of interest rates, bucketed in 0.5% ranges, weighted by loan count.*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55f03_1*ill7grxCAEPcOfo206fM9Q.png)

[Start Your Dashboard with Wren AI Today!](http://www.getwren.ai/)

Wren AI would understand the intent, write and execute the SQL, and return the chart instantly.

- **Elimination of the Query Builder:** For a structured finance professional who isn’t an expert in data modeling or SQL, the query builders, while user-friendly, can still be a barrier. Wren AI completely removes this, allowing anyone to get answers, from a portfolio manager to a client success representative.

### 2\. The Power of Generative BI

**Automated Insights:** Wren AI doesn’t just return data; it can generate narrative summaries of charts and reports. It can explain **“why”** a trend is occurring, such as:

***Estimate the portfolio charge-off impact if borrowers with credit score under 620 had a 15% higher default rate.***

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55ef7_1*6q7cFX1WoXEkswNVCIgcDQ.png)

[Start Your Dashboard with Wren AI Today!](http://www.getwren.ai/)

***How do default rates vary by credit score ranges across different counties?***

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55ef4_1*b5Ndrv9d3qoZtg2QRE2TzA.png)

- **Context-Aware Follow-Up:** Wren AI’s conversational nature allows for a fluid, back-and-forth exchange.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55f22_1*HBP0wHM7mNMn9qzlyiDNWg.gif)

[**What should I ask next?**](http://www.getwren.ai/)

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68d2543df55a50ed2ae55efa_1*J7KLrlaOP0oK6k3KdHttXw.png)

**How do default rates vary across different asset classes of loans (e.g., Mortgage, SME, Consumer) in the top five states by loan volume?**

This eliminates the need to restart a new query or build a new dashboard for every follow-up question.

### 3\. True Embedded Analytics

- **API-First Approach:** Wren AI is built for embedding. Analysts could integrate Wren AI’s API directly into its core platform. Instead of a separate tab for “BI & Analytics,” a user could see a chat window or a search bar next to their portfolio data. This allows for in-context insights without ever leaving the core application.
- **Personalized & Role-Based Insights:** Wren AI can be configured to understand a user’s role and permissions. This ensures that a portfolio analyst can ask for granular details on a specific loan pool, while a senior executive might only see high-level summary data, all through the same interface.

**Wren AI** ([www.getwren.ai](http://www.getwren.ai/)) helps you ask questions that go *beyond* the dashboard. It turns a static, pre-defined view into a dynamic, interactive conversation with the data itself.

## **Try Wren AI Cloud Free for 7 Days or Explore On-Premises Solutions!**

Ready to simplify your data analysis with Wren AI? Try Wren AI Cloud free for 7 days at [getwren.ai](https://getwren.ai/) and experience seamless text-to-SQL querying on your Snowflake database. Prefer a private, on-premises solution? Contact us at [contact@getwren.ai](https://getwren.ai/post/) to learn more!

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Beyond Dashboards How Conversational AI is Revolutionizing Structured Finance Analytics

Imagine this: you’re running an ad campaign with millions of impressions flying across dozens of exchanges and DSPs. The auction is over in milliseconds, money is spent instantly, and your manager wants to know

> *“What is the win rate for DV360 last month”*

You log into dashboards, wait for reports, maybe even ping your data team. By the time you find the answer, the opportunity to fix it has already passed.

This is the reality of today’s ad marketplaces — powerful, but often too slow for the speed of real-time bidding.

## What is an Ad Marketplace?

An ad marketplace, often called an ad exchange, is a digital platform where publishers (website owners or app developers) sell ad space and advertisers buy it through automated auctions.

This real-time bidding (RTB) process happens in milliseconds, matching supply and demand with incredible efficiency. But behind the scenes, it’s also **data-heavy and time-sensitive**. Data flows from publishers, DSPs, and data centers — and managing this complexity is where challenges begin.

## The Challenge: Drowning in Data, Starving for Insight

Here’s what slows the industry down:

- **Fragmented data:** data across warehouses, servers, and BI tools.
- **Manual processes:** rebuilding dashboards, reconciling IDs, pulling hourly logs.
- **Metric confusion:** teams define win rate, bid rate, or eCPM differently.
- **Slow insight loops** : analysts write SQL just to answer simple questions.
- **Accessibility gaps:** non-technical teams wait for answers from data teams.

When milliseconds matter, these bottlenecks cost money.

## **The Breakthrough: No SQL, No Delay**

That’s where Wren AI comes in. Instead of relying on dashboards or analysts, anyone — technical or not — can type a question in plain English and get an answer in seconds.

**What it delivers:**

- **Natural language queries**: type your question the way you’d say it.
- **Instruction-based accuracy**: add metric rules to ensure consistent outputs and keep every team aligned.
- **Instant insights**: spot outliers, trends, or regional performance shifts immediately.
- **Self-serve dashboards**: update as new data lands, no engineering tickets required.

And because Wren AI can connects through **Trino**, it can pull data directly from BigQuery, S3, or Postgres — without building new ETL pipelines.

## A Tale of Two Perspectives

*(The sample dataset used in this post was generated by Grok with randomized values for illustrative purposes and does not represent actual ad marketplace data.)*

**Publisher side — faster yield optimization**  
Instead of waiting on reports, a yield manager can ask:

*“Which exchanges deliver the highest chosen-bid rate during evening hours in California?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a349_1*uaCM24T2vU_6wTnoIDdK8A.png)

*“Which hour has the highest spend data center?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a35f_1*uXWBdVkbEUUYlm5eXgUtyA.png)

*“Show top 5 DSPs with high bids but low impressions on site\_47.”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a362_1*4MsbB_kXFISKfFPfJO9Mdw.png)

Result: floors and traffic allocation adjusted in real time, boosting revenue.

**DPS side — smarter pacing & efficiency**  
A media buyer replaces spreadsheets with questions like:

*“Show win rate and eCPM by DSP; rank worst to best.”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a36d_1*PyhLF3HkMjTyqlPM0XTfHw.png)

*“When did DV360’s win rate drops >20% in Texas between 6–10pm.”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a359_1*kdWoYwHtIp6eiAX-hUw9Yg.png)

*“Which site\_id and exchange combos give best impressions per dollar in New York?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a370_1*zXxT0m2jWJlY5qw9h8Nhpw.png)

Result: faster reallocations, earlier risk detection, and transparent reporting that business teams can run themselves.

## Ensuring Unified Metrics: Using Instructions in Wren AI

The real magic isn’t just speed — it’s **consistency**. With Wren AI, teams define metrics once.

Metrics:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a34c_1*USozYYS23pX-1zVE3kG6Ng.png)

Patterns:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a34f_1*HpSDzztaXcPsSnrlN3nYqA.png)

## Wren AI — A Game-Changer for Ad Marketplace Efficiency

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68b85e0a20be6a4d4910a352_1*u_aFAXvEP8wgfLXQ_sDAGw.png)

In real-time advertising, delays cost money. By the time yesterday’s dashboards load, today’s auctions are already over.

Wren AI flips the script:

- Non-technical users can self-serve answers instantly.
- Analysts save hours of manual work.
- Teams act faster, with confidence in their metrics.

It’s not just about efficiency — it’s about competing in a market where milliseconds matter. Because in ad marketplaces, speed doesn’t just win bids.

Speed wins the future

**Ready to transform how your organization accesses data?** [**Request a demo or start your free trial at getwren.ai**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=ads)

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Milliseconds to Millions Wren AI That Turns Ad Marketplace Chaos into Speed and Clarity

*We’ve been getting questions about how* [*Wren AI*](https://github.com/Canner/WrenAI) *compares to* [*Vanna*](https://github.com/vanna-ai/vanna)*, two of the most popular open-source text-to-SQL solutions. While both tackle the same surface problem of turning natural language into SQL, they have fundamentally different visions and serve very different audiences. This comparison will help you understand which approach fits your needs.*

> *From Query Generators to Business Intelligence: How Wren AI’s Enterprise Architecture and Semantic Layer Deliver Production-Ready Analytics That Vanna Can’t Match*

## Why This Comparison Matters

Over the past few months, we’ve received countless questions from data teams, CTOs, and developers asking: *“What’s the real difference between Wren AI and Vanna? Which one should we choose?”*

The confusion is understandable. Both are popular open-source projects that convert natural language into SQL. Both have strong GitHub communities and active development. But that’s where the similarities end.

**The fundamental difference:** [Vanna](https://github.com/vanna-ai/vanna) is a powerful **component library** designed for developers who want to embed text-to-SQL functionality into custom applications. [Wren AI](https://github.com/Canner/WrenAI) is a complete **AI-driven business intelligence platform** built for organizations that need to scale data access across business teams with enterprise governance.

> Think of it this way: if you need an engine to build your own car, choose Vanna. **If you need a complete vehicle that’s ready to drive your entire organization forward, choose Wren AI.**

This article will break down exactly when and why each approach makes sense, so you can make the right choice for your specific situation.

**TL;DR:** Vanna excels as a lightweight text-to-SQL library for developers who want to embed SQL generation into custom applications. Wren AI is a complete Generative Business Intelligence platform built for enterprise teams who need governance, semantic consistency, and business-ready insights, not just SQL strings.

If you’re building internal tools and need raw SQL generation, Vanna is excellent. If you’re scaling data access across business teams while maintaining enterprise controls, Wren AI delivers the complete solution you actually need.

## The Open-Source Landscape: Beyond Simple Text-to-SQL

The explosion of open-source text-to-SQL tools solved one critical problem: removing the SQL barrier that blocked business users from their data. But generating a SQL string is just the first step in a much longer journey to actionable insights.

[**Vanna**](https://github.com/vanna-ai/vanna) **(**[**GitHub**](https://github.com/vanna-ai/vanna) **— 20k stars**, started in 2023/07**)** is an early project, developer-friendly text-to-SQL with impressive GitHub traction and MIT licensing that makes it perfect for embedding in custom applications.

[**Wren AI**](https://github.com/Canner/WrenAI) ([**GitHub**](https://github.com/Canner/WrenAI) **— 12k stars**, started in 2024/05) takes a fundamentally different approach: building a complete Generative Business Intelligence platform that handles everything from natural language questions to governed dashboards and embedded API— all within a single stack.

## Fast Facts Comparison

![Article content](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68ad878141e0c097b277406e_1756189780223.png)

## 1\. The Enterprise Reality: Why Semantic Layers Matter

### Wren AI’s Semantic-First Architecture

Coming from Canner Team’s experience serving large organizations, Wren AI understands that **context is everything** in enterprise data:

- **Business Terminology Mapping:** Define “Monthly Recurring Revenue,” “Active Users,” or “Churn Rate” once; every query thereafter uses consistent business logic
- **Relationship Governance:** Explicit joins and dimensions eliminate hallucinated columns and incorrect relationships
- **Unified SQL Interface:** Wren AI rewrites queries for different database dialects while maintaining semantic consistency.

> ***Real Customer Impact:*** *“Before Wren AI, different teams had different definitions of ‘revenue,’ and no one had formally codified what that meant. Wren’s semantic layer helped us define revenue once, in one place, creating our single source of truth.”*

### Vanna’s RAG-Based Approach

Vanna improves accuracy through retrieval-augmented generation (RAG), pulling schema snippets and documentation at runtime. While effective for many use cases, this approach has limitations:

- **No formal business logic:** Relies on prompt engineering and example curation
- **Inconsistent interpretations:** Same terms may be understood differently across queries
- **Manual governance:** Requires ongoing curation to maintain quality

> ***When This Matters:*** *Enterprise teams need* ***semantic consistency****. When a CFO asks for “Q4 revenue” in January and a product manager asks the same question in March, they must get identical results using identical business logic.*

## 2\. Complete GenBI Workflow vs. Component Library

### Wren AI: End-to-End Intelligence Platform

Wren AI delivers a complete workflow that mirrors how business users actually think:

- **Natural Language Question** — “What’s our monthly churn rate by customer segment?”
- **Contextual SQL Generation** — Using the semantic layer and business definitions
- **Automatic Visualization** — Charts render instantly with appropriate chart types
- **Embedded AI API** — Call a single REST or SDK endpoint to turn plain-language questions into governed SQL, visualizations, and GenBI summaries
- **Dashboard Integration** — Save results to live dashboards with scheduled refreshes
- **Governance & Audit** — Full tracking of who asked what, when

> ***Perfect For:*** *Data teams at growing companies drowning in repetitive reporting requests who need to scale insights without scaling headcount.*

## Vanna: Flexible Component for “Developers:

Vanna excels as a building block for custom applications:

- **Clean API** for SQL generation
- **Framework flexibility:** Works with Streamlit, Flask, or custom UIs
- **MIT licensing:** Embed anywhere without restrictions
- **Developer control**: Build exactly the experience you want

> ***Perfect For:*** *Development teams building custom analytics into existing applications who want to control every aspect of the user experience.*

## 3\. Enterprise Security & Governance: Built-In vs. Bolt-On

### Wren AI Enterprise’s Enterprise-Grade Security

Built from day one for enterprise deployment:

- **Role-Based Access Control** — Define what each user role can access
- **Row-Level Security** — Users only see data they’re authorized to view
- **Column-Level Permissions** — Hide sensitive fields automatically
- **Complete Audit Trails** — Track every query and user interaction
- **On-Premise Deployment** — Full control over data and infrastructure
- **Compliance Support** — GDPR and industry regulation alignment

### Vanna’s Developer-First Security

As a library, Vanna inherits the security model of your implementation:

- **Application-level security** — you control authentication and authorization
- **Deployment flexibility** — secure it however you want
- **Custom audit trails** — build logging as needed
- **Integration security** — works with your existing security infrastructure

> ***The Enterprise Gap:*** *While Vanna can be secured, Wren AI provides enterprise security patterns out-of-the-box, reducing implementation time and security risks.*

## 4\. Real-World Use Cases: When Each Tool Shines

### Choose Wren AI When:

**Scaling Data Access Across Business Teams**

- *Product managers need user behavior insights without pulling data engineers*
- *Marketing teams want campaign performance analysis on demand*
- *Executive dashboards require consistent, governed metrics*

**Complex Multi-Source Analytics**

- *Querying across CRM, data warehouse, and product analytics simultaneously*
- *Real-time insights from live data sources without ETL*
- *Unified business logic across disparate systems*

**Regulatory & Compliance Requirements**

- *Financial services need complete audit trails*
- *Healthcare organizations with strict data access controls*

### Choose Vanna When:

**Building Custom Analytics Applications**

- *Embedding text-to-SQL into existing products*
- *Creating domain-specific analytics tools*
- *Prototyping text-to-SQL functionality quickly*

**Developer-Led Data Science Workflows**

- *Jupyter notebook integration for data exploration*
- *Custom RAG pipeline experimentation*
- *Single-database, technical user scenarios*

**Maximum Licensing Flexibility**

- *Commercial products requiring MIT licensing*
- *Closed-source applications needing unrestricted embedding*
- *Cost-sensitive projects requiring no enterprise licensing*

## 5\. The Future of Generative BI

Both tools represent different visions for how AI will transform business intelligence:

**Wren AI’s Vision:** AI as a complete intelligence layer that replaces traditional BI interfaces with conversational experiences, embedded everywhere users work.

**Vanna’s Vision:** AI as a powerful component that developers embed into custom applications, maintaining maximum flexibility and control.

## Making the Right Choice for Your Team

### Choose Wren AI If:

- You need **enterprise-ready GenBI** with governance built in
- Your goal is to scale **data access** across business teams
- You want **faster time-to-value** with proven enterprise patterns
- **Semantic consistency** and audit trails are non-negotiable
- You prefer **platform innovation** over custom development

### Choose Vanna If:

- You’re **building custom applications** that need text-to-SQL components
- **Maximum flexibility** and control are priorities
- You have **development resources** to build UI and governance layers
- **MIT licensing** is required for your use case
- You want to **experiment** with text-to-SQL in notebooks or prototypes

## Get Started Today

**Ready to experience enterprise GenBI?**

🚀 **Try Wren AI:** Visit [getwren.ai](http://getwren.ai/) for a free trial. Connect your database in minutes and start asking questions in plain English.

🔗 **Star** [**Wren AI on GitHub**](https://github.com/Canner/WrenAI)**:** Join 12K stars & 1.5K+ developers building the future of conversational business intelligence.

💬 **Join the** [**Community**](https://discord.gg/5DvshJqG8Z)**:** Connect with data teams already scaling their analytics with Wren AI’s semantic-driven approach.

**Remember:** The future of BI is conversational. The question isn’t whether AI will transform how we work with data — it’s whether you’ll lead that transformation or follow it.

*Don’t wait for perfect data. Start where you are, learn as you go, and let Wren AI handle the complexity of turning questions into insights.*

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Wren AI vs. Vanna: The Enterprise Guide to Choosing a Text-to-SQL Solution

This synergy of a marketing measurement platform with the integration of Wren AI, a generative business intelligence (GenBI) engine, transforms traditional analytics operations into an AI-driven ecosystem, where marketers gain instant, SQL-backed insights without analyst dependencies.

At the core of this evolution is Wren AI’s advanced AI agent, which amplifies the “AI agent force” through text-to-SQL generation, natural language querying, semantic modeling, and more. These agents form a collaborative network that automates data analysis, uncovers patterns, and delivers real-time recommendations — empowering marketers to transform raw data into strategic actions.

### 📊 Why a Marketing Measurement Platform Matters

While Wren AI provides the intelligence, it thrives when embedded within a modern **marketing measurement platform** that enables:

**Marketing Mix Modeling (MMM)** Run accurate, privacy-friendly models to understand true channel contribution.

> **Which campaigns have the lowest mCPA and why?**

![Which campaigns have the lowest mCPA and why?](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/689da0df7362760ddeaa2359_1*5TjOX8LuqoGXFdDCIFLydQ.png)

**Incrementality & Causal Testing:** Validate whether campaigns are driving *real* lift — not just clicks.

**Budget Optimization** Forecast spend scenarios, detect saturation points, and shift investment dynamically.

**Real-Time Recommendations** Leverage AI agent insights to make daily or weekly decisions that align with revenue goals.

#### Here’s how Wren AI’s AI agent is redefining intelligence for marketing measurement platforms.


### 🚀 From Data to Decision

The power of Wren AI’s GenBI engine equips the platform with a robust suite of AI agents designed for seamless, conversational analytics. These agents eliminate bottlenecks, enabling marketers to derive insights from plain-language queries in seconds. Key capabilities include:

- **Text-to-SQL Generation**: Wren AI’s agents convert natural language questions into accurate SQL queries, drawing from context-aware models. Marketers can effortlessly extract campaign and performance data for reports or analyses without writing code.
- **Natural Language Querying**: Through an intuitive chat interface, users ask questions like “What are the usage patterns for high-churn segments?” and receive instant charts, summaries, or visuals. This conversational BI is ideal for exploring insights on the fly.
- **Semantic Modeling**: Agents ensure consistent data definitions via robust modeling layers, minimizing hallucinations and enhancing accuracy. This is crucial for reliable intelligence, where precision drives decisions.
- **AI-Driven Recommendations**: Wren AI’s agents validate hypotheses, uncover hidden patterns (e.g., churn risks or revenue trends), and provide smart suggestions for optimizations like ad spend adjustments or messaging refinements.
- **White-Label and Embeddable Agents**: With embeddable APIs, Wren AI allows the platform to deploy custom, branded AI agents within workflows. This enables secure, hallucination-free interactions tailored to performance marketers’ needs.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/689da0df7362760ddeaa235c_1*FmtIEE7ZQKVFsS6dJHcgEQ.png)

These features create an “AI agent force” — a dynamic network of intelligent agents that collaborate across the data lifecycle, automating what once required data science expertise and accelerating decision-making for marketers.

### 💡 **The Big Picture: An AI-Centric Marketing Measurement Ecosystem**

By embedding Wren AI’s agents into a marketing measurement platform, performance marketers gain a unified platform for:

> **Smarter segment building with semantic precision**

> **Adaptive campaigns fueled by real-time GenBI insights**

> **Precise measurement via explained incrementality and Marketing Mix Modeling (MMM)**

> **Instant actions from AI-driven pattern detection and recommendations**

This agent-centric approach eliminates tool silos, fosters conversational analytics, and thrives in a privacy-first, cookie-less era — ultimately driving superior marketing outcomes.

### 🧩 Enhancing Integration and Companionship

Wren AI’s AI agents excel in seamless integration and serve as an ideal companion to marketing measurement platforms, enhancing existing data ecosystems without disruption.

Through its API-driven and embeddable architecture, Wren AI can be integrated directly into measurement workflows with just a few clicks, connecting to databases, data warehouses, and metadata sources effortlessly. The white-label AI agent solutions enable branded, secure deployments within platform UIs. Companies can embed these agents to deliver tailored insights under their own branding, accelerating rollout compared to in-house builds.

For performance marketers, this means validating hypotheses instantly, such as analyzing campaign ROI trends or channel saturation, while reducing analyst bottlenecks.

Wren AI acts as a proactive partner: business teams get data-backed answers in plain language, product teams validate ideas conversationally, and customer success teams deliver custom reports with ease.

Overall, Wren AI’s integration turns marketing measurement platforms into intelligent, scalable ecosystems, where AI agents not only automate analysis but also foster collaboration across teams, driving faster decisions and clearer strategies for marketers.

### 🎯 TL;DR

Wren AI ([getwren.ai](http://www.getwren.ai/))’s AI agents deliver generative BI magic: **text-to-SQL automation, natural language querying, semantic accuracy, and real-time recommendations**. This creates a collaborative “AI agent force” for closed-loop marketing, empowering performance marketers with instant insights and optimizations.


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How Wren AI Agent Supercharges the Future of Marketing Measurement Platforms

Modern game platforms run on data. Every click, coin, and campaign leaves a footprint, from ads and microtransactions to session stats and affiliate activity. These datasets hold the key to growth, but here’s the problem: the SQL queries that unlock answers are often **slow, compute-heavy, and expensive**.

Fraud detection, revenue-sharing models, retention tracking. They all require multi-table joins and deep scans. Run them inefficiently, and you’re burning credits as fast as you’re generating insights.

### The Challenge

Waiting on SQL queries doesn’t just slow down decision-making; it delays critical product and business insights.

With [**Wren AI**](https://getwren.ai/), teams can move from question to answer in seconds, using natural language to query backend data directly. This reduces reliance on analysts, shortens feedback loops, and keeps projects moving forward without the usual BI bottlenecks.

### That’s where Wren AI steps in.

As an [open-source **Generative Business Intelligence (GenBI)**](https://github.com/Canner/WrenAI) tool, Wren AI translates natural language questions into SQL that follows warehouse best practices. Instead of being its optimizer, Wren AI helps shape queries so platforms like Snowflake can execute them more efficiently. The result: lower operation costs, faster answers, and accessible analytics for both technical and non-technical teams.

### The Semantic Layer: Where Optimization Begins

Wren AI is built around a **Semantic Layer,** a system of **Instructions, Question↔SQL pairs, and semantic definitions** that guide how queries are generated.

- **Instructions:** Steer query patterns (e.g., *“filter on `session_date` and `game_id` first,” “avoid `SELECT *`,” “aggregate before joins”*).
- **Question↔SQL pairs:** Save your best, ***most cost-efficient SQL; future prompts can reuse or adapt them, avoiding reinvention and bad SQL***.
- **Metrics/semantics:** Define canonical measures like LTV, D2 retention, or cohort logic, so every query uses the same efficient formula.

The **Semantic Layer** doesn’t tune the data warehouse directly. Instead, it provides the **context and business logic** that guide the NL→SQL agent to generate higher-quality queries and reuse proven ones. The real performance improvements come when cloud platforms like Snowflake apply their own optimizations, such as partition pruning, clustering, and caching, to those well-structured queries.

That’s why Wren AI’s Semantic Layer is best understood as a **knowledge-driven enabler of consistent, efficient query generation**. It ensures teams ask questions in a way the warehouse can execute effectively, reducing errors, improving reusability, and accelerating insights across the organization.

### 1\. Making Revenue Streams Smarter

Revenue doesn’t come from one channel. It comes from **ads, subscriptions, affiliates, payment fees, and in-app events,** each with massive datasets behind them.

Wren AI helps teams explore these revenue streams by **guiding SQL generation through its Semantic Layer**, ensuring queries make use of existing warehouse features like clustering, metadata, and partition filters. Rather than tuning the warehouse itself, Wren AI enables teams to produce **smarter, more consistent queries** that align with best practices, reducing the risk of inefficient scans and accelerating time-to-insight.

That means finance and marketing teams can quickly surface insights like:

> *Which affiliates deliver the highest-value players*

> *Which subscription tiers drive retention*

> *Which in-app events generate the best margins*

### 2\. Turning Gameplay Data into Insights

Understanding how players interact in-game is just as critical as tracking revenue. Wren AI ingests granular stats like KDA ratios, session lengths, win/loss records and turns them into **actionable SQL queries**.

By applying **warehouse-friendly query patterns**, such as selective projections, partition filters, and leveraging existing metadata. Wren AI enables teams to refine and build knowledge and semantic context. These practices help minimize unnecessary full-table scans and keep compute usage efficient.

Wren AI gives teams deeper visibility into gameplay patterns while maintaining a fast, lightweight path to insights.

### 3\. Solving Analytics Pain Across Teams

Backend data isn’t just for engineers. Marketers, product managers, and executives all need quick answers, but SQL has long been a barrier. Wren AI removes that friction.

#### User Engagement:

> *Query* “Who signed in and played last week?”

Wren AI generates SQL that funnels sign-ins and sessions efficiently.

#### Transactions:

> *Query* “Show Day-2 retention for Roblox vs. Bingo Blitz”

Wren AI writes SQL that applies cohorts and partition filters to avoid heavy scans.

#### Marketing Funnels:

> *Query:* “What’s LTV for Google installs over the last 3 months?”

Wren AI ties together cost, install, and revenue tables with **efficient joins that minimize compute use**.

#### Strategic Oversight:

> Query: “Summarize revenue by country and cohort month.”

Wren AI empowers every team, not just data scientists, to explore data, test hypotheses, and make faster decisions.

### 4\. SQL Knowledge = Cost Efficiency

In modern **cloud data platforms**, compute is currency. Costs aren’t about how much data you store; they’re about how you query.

Take Snowflake’s Data Cloud as an example: every SQL query consumes credits, even if the analyst only needs a small slice of data. Wren AI doesn’t modify Snowflake’s internals, but it **guides query generation** so teams naturally take advantage of features like micro-partitions and result caching.

That’s why smarter SQL isn’t just a technical improvement, it’s a **business advantage**.

❌ **Traditional SQL**

- Risk of full-table scans
- Long runtimes
- Inefficient joins and repeated work

✅ **Wren AI–Generated SQL**

- Safe “dry runs” before execution
- Cached queries and knowledge reused across teams
- Faster runtime with reduced compute load

Unlike BI tools that reduce costs by limiting how much users can query, Wren AI enables teams to keep asking questions freely, while still producing **consistent, warehouse-friendly queries** that avoid waste and accelerate insights.

### 🚀 Final Thoughts

In today’s data-driven gaming ecosystem, speed and efficiency are everything. Wren AI enables organizations to:

- Generate **warehouse-friendly SQL automatically**
- Reduce query costs while scaling analytics
- Empower non-technical users with self-service insights
- Keep data exploration continuous without blowing through credits

Because one great SQL query is always cheaper, and more powerful than ten inefficient ones.

Whether you’re on **Snowflake** or another **cloud data platform**, Wren AI helps teams unlock more profound insights, spend smarter, and move faster.

#### Wren AI ([getwren.ai](http://www.getwren.ai/)) is your first step toward cost-intelligent querying by making every query smarter, faster, and leaner.

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GAME’s ON Unlocking Gaming Analytics How Wren AI Transforms Backend Data Into Actionable Insights

In the fast-paced world of automotive sales, **timing and visibility into inventory** are everything. Whether you’re a sales manager, regional director, or dealership rep, your ability to act quickly can **make the difference between closing a deal and losing a customer**. And it all starts with simple questions like:

*“Which models are currently in stock?”*

*“What configurations are available?”*

*“What’s selling best in my region?”*

## The Challenge: Data That’s Too Slow for the Sales Floor

Many dealerships and automotive organizations struggle with common data challenges that slow down decision-making:

- **Delayed Insights**: By the time real-time data (like inventory or lead flow) reaches decision-makers, the opportunity to act has often passed.
- **Low Usability**: Most dealerships have dashboards, but fewer than one in three are satisfied with the insights they get. For non-technical teams, these tools often remain out of reach.
- **Manual Reporting**: Teams still rely on ad-hoc reports or long waits for data requests to be fulfilled, slowing down operations and hurting customer experience.
- **Complex Inventory Management**: Thousands of vehicle configurations, fluctuating demand, and supply chain variables make optimizing stock a constant challenge.

## The Solution: Ask a Question, Get an Answer In Seconds

Wren AI changes how automotive companies interact with their data by making insights **immediate, accessible, and actionable, even for non-technical users**.

- **Natural Language Queries**: Ask questions like in plain language and get answers in seconds. No SQL or technical skills required.
- **Live, Reliable Insights**: See up-to-date inventory, trending models, or performance by location instantly, without waiting on the data team.
- **Empowered Teams**: From showroom reps to regional managers, every user can make informed decisions on the spot.

Now let’s demonstrate how to answer those key questions mentioned in the first paragraph, you can see how effortless it is with Wren AI. Simply type your query in plain English, and the AI takes care of the rest — no SQL or technical expertise required. Within seconds, you’ll have the insights you need, empowering non-technical team members to make faster, smarter decisions with confidence.

‍

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b11275fb3fea8e69ff555_9ab1d774.png)

Question: *“Which models are currently in stock?”*

‍

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b11490cb8196b905e7b0b_0316bc85.png)

*“What configurations are available?”*

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![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b116b65f51cfc4ac6a9c1_7ed1a7d5.png)

Question: *“What’s selling best in my region?”*

‍

## Wren AI in Action: Real-World Automotive Use Cases

Let’s look at how Wren AI helps dealerships and manufacturers unlock value instantly:

### Case 1: Accelerating Sales at the Dealership Level

A dealership in California struggled with lagging inventory updates. A rep using Wren AI simply asks,

*“Show me all 2025 Toyota Camrys with leather seats in stock.”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b0f5af9cf56769ce6a9ae_1*Nl6S6AAzpnSB5f2LHld_EA.png)

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Within seconds, the system returns a real-time list of vehicles, including location and availability. This instant access improves customer engagement and significantly boosts close rates.

### Case 2: Smarter Inventory Management for a Regional Chain

A regional auto group notices certain models are stagnating on lots. The inventory manager asks,

*“Which models have been in stock for over 60 days, and what’s the demand trend for them?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b0f5af9cf56769ce6a9ab_1*_w527MvIKLyF9GX0gKUbzQ.png)

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Wren AI surfaces the insights instantly, allowing the team to initiate targeted promotions and adjust ordering strategies.

## The Bottom Line: Speed Sells

‍

In the automotive world, every minute matters. Delayed data means delayed decisions and missed sales.

By giving non-technical users the ability to **self-serve analytics instantly**, WrenAI helps your team:

- Stay informed in real time
- Act fast and close deals
- Reduce dependency on overwhelmed data teams
- Deliver a smoother, smarter customer experience

Whether you’re managing a regional sales strategy or helping a customer on the lot, **WrenAI turns data into action — right when you need it most**.  Below are some charts built through Wren AI.

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![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b118825219da52787b249_dffbcaaa.png)

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b11968ce52f8b63b0713a_44790d6a.png)

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b11affb7caf1a950f06c1_61f0a69f.png)

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/688b11affb7caf1a950f06be_e150a109.png)

‍

‍

> **Ready to shift your data strategy into high gear?**

**Ready to transform how your organization accesses data?** [**Request a demo or start your free trial at getwren.ai**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=ads)

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How WrenAI Revolutionizes Automotive Inventory Management for Non-Technical Users

Yet, as data volumes soar and attention spans dwindle, traditional self-service tools often fall short, constrained by complex dashboards and rigid report structures.

Conversational BI is revolutionizing this space, making data as intuitive as a chat with a colleague. Wren AI’s GenBI platform leads this transformation, uniquely bridging the accessibility of self-service analytics, the conversational ease of a chatbot, and the power of an AI agent to deliver instant, actionable insights tailored to diverse personas.

*Which days of the week had the highest footfall?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea58_1*R77icZuybpmvCk42OKOdBA.png)

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This article explores the transformative potential of self-service analytics, its impact on key roles, and how Wren AI’s GenBI redefines the BI ecosystem.

## The Power of Self-Service Analytics

Self-service analytics empowers users to access, analyze, and visualize data without technical expertise, fostering a data-driven culture. By consolidating dashboards, reports, and metrics into user-friendly hubs, these platforms democratize insights. Key benefits include:

- **Empowerment**: Users can answer questions like “What drove last quarter’s sales?” without SQL or coding skills.
- **Speed**: Instant insights eliminate delays from data team requests.
- **Data Literacy**: Promotes data-driven decisions over intuition.
- **Cost Efficiency**: Reduces reliance on specialized analysts for routine queries.

However, traditional self-service analytics faces challenges:

- **Complex Navigation**: Dashboards can overwhelm users, requiring training.
- **Limited Flexibility**: Pre-built reports restrict ad-hoc data exploration.
- **Setup Overhead**: Data teams must prepare schemas or dashboards upfront.
- **Data Quality Risks**: Without robust governance, users risk misinterpreting data.

Conversational BI addresses these hurdles, making self-service analytics intuitive and dynamic. Wren AI’s GenBI bridges the gap by combining the accessibility of self-service platforms, the conversational simplicity of chatbots, and the analytical depth of AI agents, enabling users to ask questions in plain English and receive tailored insights instantly.

## Exploring Self-Service Analytics in Action

Self-service analytics supports a range of business questions, particularly for retail or product-driven organizations. Here are practical use cases across key areas:

## **Sales & Product Performance**:

*Which product categories had the highest total sales revenue?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea43_1*0XFnmrD43668roh_DJGesA.png)

## **Geographic Analysis**:

*Which store location had the highest average transaction size?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea49_1*x7C6hSLZb7uSVeA-Zxuqcg.png)

*How does product preference (e.g., tea vs. coffee) vary by stores?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea40_1*82262riz3iJlO9BTKCBnjg.png)

## **Time-Based Insights**:

*What were the sales trends by month ?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea3a_1*wzXaYtyXMGSTYJTQnIhUYg.png)

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## **Comparison & Growth**:

*Compare revenue from coffee vs. tea in Q1?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea31_1*csrPIrwFyz38e2lpVDZHMQ.png)

## **Operational Metrics**:

*What was the average revenue per transaction?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea34_1*2tD9oyKW6iSOmLy6iDahGA.png)

*How many distinct products are sold in each category? What are they?*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea52_1*F4RTavOw8sOfZtkHxG95uw.png)

Traditional self-service tools often require pre-built reports for such questions. Wren AI’s GenBI enables users to ask these directly, generating SQL and visualizations instantly, making it a powerful tool for dynamic exploration across personas.

## Wren AI’s Role in Self-Service Analytics

Wren AI’s GenBI redefines self-service analytics by seamlessly integrating the strengths of self-service platforms, chatbots, and AI agents. It overcomes dashboard-centric limitations by enabling raw data exploration, serving as a linchpin for personas like marketers, customer success managers, and executives. Key features include:**‍**

### ‍**1\. Conversational Queries**:

Ask, *can you refine with revenue as factor of peak sales hours, and list all 3 stores peak sales hours*

and GenBI’s Text-to-SQL generates precise SQL queries, delivering answers without technical expertise.

### **2\. Context-Aware Insights**:

The Modeling Definition Language (MDL) encodes business logic, ensuring results align with organizational context, surpassing static filters.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea55_1*vxsLnweIUUYB3g6nv59PXg.png)

### **3\. Instant Visualizations**:

Transforms query results into interactive charts and dashboards, tailored for marketing campaigns, client POCs, or executive reports.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/687a5ebf146e481a0fcbea3d_1*RWakBFpvxOypNbm-TtAfWw.png)

### **4\. Seamless Integration**:

Connects to data warehouses like Snowflake, BigQuery, and Postgres for smooth adoption.

### **5\. Client POC Support**:

Enables customer success teams to prototype dashboards rapidly during POCs, delivering tailored client insights.

### **6\. Open-Source Flexibility**:

Available under AGPL-3.0 or via Wren AI Cloud, with APIs for custom workflows, ideal for developer personas.

By merging accessibility, conversational simplicity, and analytical depth, GenBI empowers every persona to act like a data analyst, answering complex questions like “Which store sold the most coffee in Q1?” with ease.

## Wren AI in the AI-Driven BI Landscape

Wren AI’s GenBI stands out among AI-driven BI tools enhancing self-service analytics, offering a unique blend of conversational capabilities and customization:

- **Cloud-Based BI Tools**: These prioritize natural language search and curated analytics for enterprise governance. GenBI’s open-source model and MDL-driven SQL precision offer more flexibility for ad-hoc queries and POCs.
- **Embedded Analytics Platforms**: Focused on integrating analytics into apps, they prioritize seamless experiences. GenBI’s broader database support and APIs excel in internal BI and real-time exploration.
- **Business System Integration Tools**: These target CRM/ERP integrations. GenBI’s real-time queries and open-source nature support broader, developer-led analytics.
- **Open-Source AI Frameworks**: These offer customizable data solutions but lack GenBI’s purpose-built BI interface and semantic engine for self-service analytics.

GenBI’s open-source accessibility, context-aware querying, and POC support make it a leader for persona-driven, conversational BI.

## Use Cases: Where Wren AI Excels

- **Routine Queries**: Chatbots handle basic lookups like “What’s today’s revenue?” but lack analytical depth.
- **Structured BI Needs**: Traditional self-service analytics suits curated dashboards for executives or marketers.
- **Dynamic Analytics and POCs**: GenBI shines for ad-hoc queries (e.g., “Which products showed the highest growth in Q1?”) and client-facing POC dashboards.

## Fostering a Data-Literate Culture

Wren AI’s GenBI makes data a natural part of decision-making, closing the gap between questions and answers for all personas. It scales data specialists’ impact, empowering users to explore confidently. By bridging self-service analytics, chatbot simplicity, and AI agent power, GenBI fosters a culture where data is a collaborator, sparking innovation across teams.

## The Future: Self-Service Analytics Meets Conversational AI

The future of BI lies in blending self-service analytics with conversational AI. Traditional platforms provide structure, but AI agents like Wren AI’s GenBI unlock limitless data exploration for marketers, customer success managers, and executives. Compared to other BI tools, GenBI’s open-source flexibility, MDL-driven precision, and POC support make it a standout. As trends like embedded analytics and governance grow, GenBI positions self-service analytics as accessible and powerful for every role.

Ready to transform your BI workflow? Wren AI’s GenBI — open-source, cloud-ready, and built for scale — lets you chat with data, generate insights, and support client POCs effortlessly. 👉 *Try Wren AI GenBI today at* [*getwren.ai*](http://getwren.ai/)*.*

## *Talk to your data. Build smarter.*

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Conversational BI Empowering Self-Service Analytics with Wren AI’s GenBI

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6cdeef58823486f50ea4_api_preview.webp)

### ***What if your CRM could answer complex questions in seconds, using natural language?***

Customer Relationship Management (CRM) systems are powerful data hubs. But when it comes to analytics, most traditional CRMs still rely on rigid dashboards, static filters, and limited automation. Business users often face delays, relying on technical teams for insights they need right now.

It’s time to rethink what analytics in a CRM should look like.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd3d_1*bPsY0NEqR-SRIxi-72MVEg.png)

[https://huggingface.co/datasets](https://huggingface.co/datasets)

This article explores how modern API-based tools can elevate CRM performance, delivering real-time, personalized insights. As a practical example, we’ll show how **Wren AI** — a generative business intelligence (GenBI) platform — enhances traditional CRM systems through **natural language querying, dynamic chart generation**, and **automation-first design.**

### Why Traditional CRM Reporting Holds Users Back

Let’s face the truth, even the most advanced CRMs often struggle with:

- **Static dashboards** that require admin setup
- **Complex report builder**s that intimidate non-technical users
- **Delayed insights** due to dependence on analysts
- **Fragmented data** stuck in silos
- **Clunky permission systems** that require custom configuration

These limitations aren’t just frustrating — they reduce agility and decision-making speed.

### From Manual Reports to Generative Analytics

What if your CRM could:

- Let users **ask questions in plain language**
- Automatically **generate SQL behind the scenes**
- Visualize results on the fly — **without needing Tableau or Power BI**
- Enforce **data security and role-based permissions** at the query level
- Work across multiple data sources — not just CRM-native databases?

That’s exactly what tools like **Wren AI** are enabling. Strong focus on providing APIs to embed its conversational AI, natural language to SQL capabilities, and chart generation directly into other applications and workflows.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd40_1*a1-xLUuUhx91fmOli8KphQ.png)

[https://docs.getwren.ai/cloud/guide/api-access/overview](https://docs.getwren.ai/cloud/guide/api-access/overview)

## Natural Language Querying Without Learning SQL

### Traditional CRMs:

Even with drag-and-drop tools, building a report in a CRM like Salesforce or HubSpot often requires understanding schema fields, relationships, and data structures.

### Modern API Approach (Example: Wren AI SQL API):

Users can simply type:

*revenue trend over time*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd50_1*yDO0xw9STayAx15CXc0kuQ.png)

## Dynamic Visualizations Without a Dashboard Builder

### Traditional CRMs:

Most CRMs require pre-configured dashboard widgets. Want a new comparison chart? File a request or build it yourself — if you have access.

### API-Based Charting (Example: Wren AI Chart API):

Send your query result to an endpoint like [api/v1/generate\_vega\_chart](https://wrenai.readme.io/reference/cloud_post_generate-vega-chart), specifying the chart type: The API interprets the intent using a business context schema (MDL), then generates:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd4a_1*d2kSlNev9i1ad2GQr_I7rQ.png)

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The API returns an embeddable visualization , instantly integrated into your CRM or internal portal.

## Automate Daily Workflows — No Scripts Needed

### Traditional CRMs:

To schedule a daily lead report in **Zoho** or **Salesforce**, you may need third-party tools, **Deluge** scripting or **Flow Builder**.

### API-Based Automation:

Automate this lead conversion query: connect the result to **Slack**, email, or CRM notification using **Zapier** or a webhook.

## Secure Insights with Query-Level Governance

### Traditional CRMs:

Setting permissions in most CRMs involves creating role hierarchies, filters, and visibility rules — prone to error and complexity.

### Role-Aware Query Filtering (Example: Wren AI’s MDL & SQL API):

The engine applies access control at the SQL level. This ensures **only authorized data is shown**, simplifying compliance with GDPR, SOC2, or CCPA.

## Integrate Data Across Platforms Without ETL

### Traditional CRMs:

Joining CRM data with ERP, marketing, or web analytics data often requires third-party ETL tools like Fivetran or Segment.

### Unified Data Queries (Example: Wren AI SQL API across Snowflake, BigQuery, PostgreSQL):

One query can pull from multiple sources. Compare total spend with site engagement — without leaving the CRM interface.

## The Takeaway: Make Your CRM Smarter — Not Just Bigger

Wren AI’s GenBI (Generative Business Intelligence) platform, powered by its Cloud API at [cloud.getwren.ai/api/v1](https://wrenai.readme.io/reference/cloud-getting-started) with Bearer token authentication, uses conversational AI and natural language queries to unlock deeper insights effortlessly. By addressing longstanding data challenges, Wren AI empowers non-technical teams to make smarter decisions faster. In this blog, we’ll explore the problems businesses faced before Wren AI and how its innovative features deliver smarter, more efficient solutions.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd43_1*KqpJRbVUnklSZs_uBjDWnA.png)

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd4d_1*DaO-TcjuROcPJk8I4n5iXw.png)

### Generative BI tools like **Wren AI** don’t replace your CRM. Instead, they unlock its potential.

*Instead of reports, you get insights.  
Instead of static dashboards, you get live, flexible charts.  
Instead of technical blockers, you get real-time, natural language access.*

In short: **your CRM becomes conversational, intelligent, and fast.**

## Wren AI in Action: A Real-World Use Case

Imagine a mid-sized e-commerce business using a Customer Intelligence Platform to track web, email, and transactional data. The marketing team wants to identify products driving repeat purchases. Before Wren AI, analysts would write SQL queries, process data overnight, and manually build dashboards, delaying action by days. Data silos and poor quality further skewed results.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/686c6c8d7bdfcafd76debd57_1*cQ14-Njb7Tc7BRhYPkRmLQ.png)

With Wren AI, the team asks:

*Show repeat purchase trends by product category for Q1 2025.*

**The Text-to-SQL API generates the query instantly, the Text-to-Charts API creates a visualization, and clean, unified data ensures accuracy — all in seconds**. The team launches a targeted email campaign using the platform’s activation tools, achieving a 20% higher conversion rate. Wren AI’s scalable, secure infrastructure supports this growth without complexity.

Wren AI’s GenBI platform and Cloud API revolutionize customer intelligence by addressing longstanding challenges: complex queries, siloed data, slow analysis, poor data quality, limited real-time access, and scalability issues. By democratizing data with natural language queries, enabling instant visualizations, and automating integration and cleansing, Wren AI empowers teams to make smarter decisions faster. Its secure, scalable infrastructure ensures enterprise-ready performance without complexity.

### Ready to transform your customer intelligence strategy?

Wren AI Cloud API Demo on [Hugging Face](https://huggingface.co/spaces/getWrenAI/wrenai-cloud-api-demo) turns natural language into SQL and Vega-Lite charts, with real-time streaming and tailored insights. Explore docs at [wrenai.readme.io/reference/cloud-getting-started](https://wrenai.readme.io/reference/cloud-getting-started) and join data pros unlocking insights!

Explore how Wren AI unlocks your platform’s full potential. Start a free trial [www.getwren.ai](http://www.getwren.ai/) today and experience AI-driven insights firsthand.

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Unlock CRM Superpowers with Generative BI Real-Time Insights, Natural Language Queries, and Seamless API Integration

For marketing and advertising agencies, timing is everything. Being able to instantly understand how a campaign is performing — and adjust in real time — can be the difference between wasted spend and high ROI. Teams need fast, reliable insights to evaluate ad effectiveness, optimize creative, and shift strategy on the fly.

Traditional BI tools often slow this process down. Teams have to extract data, select the right columns, build dashboards, and wait for visualizations to load. By the time insights are surfaced, the moment to act may have passed, and the budget might already be spent on the wrong audience, channel, or creative.

That’s where Wren AI comes in. With natural language querying, real-time charting, and smart metric modeling, Wren AI helps teams skip the BI bottleneck. You can simply ask a question in plain language and get a visualized, accurate answer in seconds.

# From Raw Data to Instant Insights

For advertising and marketing teams, performance metrics are everything. Whether it’s CPC, CTR, ROAS, or campaign-specific KPIs, decision-makers need fast answers to optimize spend and creative direction.

Getting started is easy. Upload a CSV file or connect your own database, complete a lightweight modeling step (like setting up semantic labels and table relationships), and you’re ready to query!

With [Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=ads), if your organization uses custom metrics, you can define them using the Knowledge feature. Just provide a simple instruction or formula, and Wren AI will learn how to calculate and return the metric exactly as you want, ensuring consistency across every question you ask.

To show how Wren AI supports performance marketing teams, here are examples of how you can define and explore two essential ad metrics: CTR and CPC.

*(sample data from Kaggle :* [*https://www.kaggle.com/datasets/rahulchavan99/marketing-campaign-dataset/data*](https://www.kaggle.com/datasets/rahulchavan99/marketing-campaign-dataset/data)*)*

- **CTR:**
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8d8f4e6bf312fcaef1_1*2WRLL18N2SQvYcS7QTWSFg.png)

Instruction for CTR

Sample questions you can ask:

#### *“What’s the CTR for each channel?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaec4_1*exy2YPGBdyzddUzgHiWXvg.png)

#### *“What’s the top 10 creative with the highest CTR?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaec1_1*KAmeBrwvAhkw7xDzWyn0XA.png)

#### *“Can you provide the top 3 creatives with the highest CTR for Facebook Ads?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8d8f4e6bf312fcaef7_1*DVGbmx5MF3af1IsGbsqfDA.png)

- **CPC**
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaebc_1*huPqltBfPo4R92DcNSFU9Q.png)

Instruction for CPC

Sample questions you can ask:

#### *“What’s the CPC for each platform?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaed9_1*tb0EcfWxS20AFvCLPzVLzQ.png)

#### *“What’s the CPC for each channel in quarter 4 in 2022?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaedf_1*pi_I9kJFQIv_3LvKdaqLVA.png)

#### *“Can you chow me the cpc in each month for each channel over the year?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8d8f4e6bf312fcaee9_1*el42liYD3N5x-1NwNmFepw.png)

# Beyond Basic Metrics

Wren AI isn’t just for ad performance. Agencies can use it for:

- **Client Management:**

*“Please calculate the total revenue of contracts and the number of deals by lead source.”*

*“Show me the list of clients who engaged with us via email in the past 3 months.”*

*“List all campaigns from last month and sort them by total revenue generated.”*

- **Sales & Funnel Analysis**

*“Give me the conversion rate from MQL to SQL by channel.”*

*“How many deals are currently in the pipeline by stage?”*

*“Show me the list of lost deals with MRR equal to or higher than 5000USD?”*

# Drill Deeper with Spreadsheet View for Complex Queries

And when you’re working with more complex questions — or need to download and manipulate your results — you can open the output directly in Wren AI’s Spreadsheet view. This gives you the flexibility to sort, filter, and annotate your data just like you would in Excel, without writing a single line of SQL or waiting for someone to build a custom report.

Here are a few examples of questions**:**

#### *“Can you show me the keyword that performs best on each platform and channel?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaedc_1*XdeYaiippG4-_9Ebz6-yiQ.png)

#### *“Which keyword and creative combination has the best performance?”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaed6_1*4YX6XQBZ-2mIR7KR2N06_g.png)

# Build the Story, Not Just the Chart

Wren AI allows you to pin any chart directly into a dashboard. As you explore questions, you build a visual narrative of campaign health and business performance. In just a few clicks, you’ll have a dashboard that tells the full story — ready to share with clients or leadership.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaecf_1*X5o0QkNHV_q0RiBeGxhCYQ.png)

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685cbd8c8f4e6bf312fcaec7_1*MHPnib8jaFOLS5IHWY6MnQ.png)

# Why It Matters

Speed and clarity are critical in marketing. Wren AI gives you both. Unlike traditional BI tools that require technical expertise and lead time, Wren AI empowers your team to act immediately, without friction.

Whether you’re optimizing creative, tracking ad spend, or managing client accounts, Wren AI makes working with data feel as fast as having a conversation.

Wren AI helps turn your ad data into action — not tomorrow, not next week, but right now.

**Ready to transform how your organization accesses data?** [**Request a demo or start your free trial at getwren.ai**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=ads)

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Less Waiting, More Winning How Smart Marketing Agencies Use AI to Drive Results Faster

*A fast-growing cryptocurrency brokerage revolutionized their trading operations by integrating Wren AI’s Generative Business Intelligence (GenBI) platform. The implementation eliminated traditional BI bottlenecks, enabling non-technical users to access real-time trading insights through natural language queries, resulting in enhanced client engagement and operational efficiency.*

# **The Challenge:** Breaking Down Data Silos in Crypto Trading

The brokerage needed a white label trading platform that could combine real-time trade data with client information to drive operational efficiency and improve customer engagement.

The platform had to provide intuitive analytics for traders and brokers while maintaining scalability and flexibility to adapt to market demands. Additionally, the brokerage wanted to empower its team with actionable insights without requiring extensive technical expertise.

# **The Solution**

[Wren AI’s Generative BI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=case_trading) (GenBI) served as the cornerstone of this transformation. Its semantic layer bridged the gap between complex data structures and natural language queries, allowing non-technical users to gain valuable insights.

Wren AI’s semantic layer translated complex data structures into intuitive, business-friendly terms, allowing users to query data in natural language without needing technical expertise.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b09245afe5597f58e5_0*OMyGedTA04K82ul4.png)

The user can easily (Text-To-SQL & Chart) query without relying on traditional BI tools (e.g., Tableau, Power BI, Looker) or manual data processes, involving SQL queries, data analysts, and static dashboards.

*“Show me the top 10 traders by trade volume this month”*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68521f153570051f0b85fb52_1*9nO4rHeje4qTq05I9XCzbw.png)

*“Which clients haven’t traded in the last 30 days?”*

*“What are the most popular trading pairs by order count this week?”*

*“Show me support ticket trends for active traders this quarter.”*

This streamlined, enhanced CRM customization by tailoring client interactions, and optimized marketing strategies through targeted campaigns based on real-time data…. No dashboard navigation. No waiting for reports. No “can you pull this data for me” Slack messages.

‍

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/685222effb7001f33ec256f9_1*svMFu0sFeWoZct2R-9xCmw.png)

‍

# What Actually Changed with [Wren AI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=case_trading)

## **Unified Data Integration**:

*Trade data (orders, amounts, and pairs) and CRM data (client profiles and account details) were seamlessly linked, creating a holistic view of client activity.*

## **Conversational Analytics with GenBI**:

*The platform’s GenBI interface enabled non-technical users to extract insights through simple, natural-language queries, reducing reliance on data analysts.*

## **Real-Time Insights**:

*The system provided up-to-date analytics on trading patterns, client behavior, and market trends, empowering the brokerage to respond swiftly to opportunities.*

## **Scalable Infrastructure**:

*The white label solution was designed to handle growing transaction volumes and client bases, ensuring long-term reliability. By leveraging the integrated trading platform and GenBI, the brokerage achieved significant improvements:*

## **Enhanced Client Engagement**:

*Personalized CRM strategies, driven by GenBI insights, increased client retention and satisfaction.*

## **Operational Efficiency**:

*Real-time analytics reduced the time spent on manual data analysis, allowing the team to focus on strategic initiatives.*

## **Improved Decision-Making**:

*Brokers and managers accessed tailored insights instantly, enabling faster and more informed trading and marketing decisions.*

## **Competitive Edge**:

*The platform’s advanced capabilities differentiated the brokerage in the crowded market, especially cryptocurrency, in attracting new clients.*

# Why This Actually Matters for Your Business

Most crypto brokerages are still stuck in the old model — hire analysts, build dashboards, wait for insights. Meanwhile, markets move in milliseconds and client expectations keep rising.

The firms winning right now aren’t necessarily the ones with the most data. They’re the ones who can act on their data the fastest.

When your competitor takes three days to identify at-risk clients, and you can do it in three seconds, guess who keeps more customers?

Wren AI’s GenBI isn’t just a BI tool, it’s an intelligent assistant for trading and client management.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/68521f153570051f0b85fb4d_1*2MkK4UGgYKrf8H98TseHiw.png)

Charting capability of Wren AI, just ask get results and charts.

Under the hood, Wren AI’s semantic layer does the heavy lifting — translating business questions into proper database queries automatically. It connects trading data with CRM data, handles the complex joins, and serves up answers in plain English.

The platform scales with transaction volume, integrates with existing systems, and doesn’t require your team to learn new tools. It just works.

# What’s Really Changing in Fintech

This isn’t just about one brokerage getting better reports. It’s about a fundamental shift in how financial firms operate.

The old way: collect data → hire analysts → build dashboards → maybe get insights → hopefully make decisions

*The new way: ask questions → get immediate answers → make decisions → repeat*

Companies still building data teams and complex BI infrastructure are solving yesterday’s problems. The winners are the ones making data access as natural as having a conversation.

# Ready to Stop Waiting for Your Data Team?

If you’re tired of being three steps behind your own business, it might be time to let your data speak for itself.

Ready to transform how your organization accesses data? Request a demo or start your free trial at [**getwren.ai**](http://getwren.ai/)

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How AI-Powered Analytics Transformed a Brokerage’s Trading Platform

In today’s AI-powered analytics landscape, the way we interact with data has undergone a significant transformation. Two approaches have emerged: uploading spreadsheets to conversational AI tools like ChatGPT and using GenBI solutions that connect directly to live databases. While both promise to democratize data access, they serve fundamentally different use cases and come with distinct trade-offs.

This guide explores which approach delivers the most value for different scenarios, with a special focus on enterprise requirements around scale, security, and data governance.

# Static Document-based Answers: Quick Insights from Static Data

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f582ac8a8bb8fbd333_1*0HfVTOs2Py26da70rVCcOw.png)

Upload a CSV or spreadsheet, asking questions from ChatGPT

One of ChatGPT’s most popular features allows users to upload Excel or CSV files and ask natural language questions about the data:

- “Which products had the highest profit margin last quarter?”
- “Show me customer churn trends by region.”

This approach has gained traction for its simplicity and accessibility to non-technical users.

# Advantages of the File Upload Approach

- **Zero Learning Curve**: Anyone comfortable with spreadsheets can get started immediately
- **No Setup Required**: Works without database connections or IT involvement
- **Visual Context**: Some platforms provide visualizations alongside text responses
- **Self-Contained Analysis**: Great for one-off projects with defined datasets

# Limitations That Businesses Quickly Encounter

The file upload approach shines for individual productivity and ad-hoc analysis, but struggles to meet enterprise requirements for automation, scalability, and compliance.

- **Data Freshness Issues**: Analyses based on point-in-time exports become outdated quickly
- **Size Constraints**: Most platforms limit file sizes (typically 10–25MB)
- **Complexity Barriers**: Complex relationships between data are difficult to represent in flat files
- **Manual Workflows**: Requires extracting, cleaning, and uploading data repeatedly
- **Data Governance Concerns**: All sensitive information may be exposed to third-party AI systems
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f5f3cf49a9b933e475_1*BdpxolJzk9uNL9JdsiwwHw.png)

Static vs. Dynamic Data Access

# Text-to-SQL (GenBI): The Enterprise Approach to Natural Language Data Access

Text-to-SQL technology translates natural language questions into SQL queries that execute against your live databases. Instead of working with data snapshots, users interact directly with up-to-date information in their data warehouses.

Example queries might include:

- “Calculate monthly recurring revenue growth by customer segment for the past year”
- “Show me conversion rates by marketing channel, excluding internal traffic”
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f6ce17a63e6c7d90ac_1*Fu4_EOrlwIqMlH-_Eao5SA.png)

Ask any business questions from databases using [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql)

# Why Text-to-SQL Delivers Superior Business Value

The key advantages of using Text-to-SQL for enterprise analytics is to ensure up-to-date insights, scales with large and complex datasets, maintains strict data security and governance standards, and integrates naturally into business workflows — empowering teams to perform advanced analysis with ease and confidence.

Some benefits below:

- **Always Fresh Data**: Access real-time information without manual exports
- **Enterprise Scale**: Handle terabytes of data across complex database schemas
- **Security-First Design**: Keep sensitive data within your existing infrastructure, raw data will not directly expose to AI models, data query through SQL.
- **Workflow Integration**: Embed natural language querying into dashboards, reports, and applications
- **Advanced Analytics**: Support for complex joins, window functions, and custom business logic
- **Governance Compatibility**: Role-based access controls and audit trails

# Where Each Approach Excels

Both have it’s use cases and scenarios, file uploads are ideal for ad hoc analysis, working with external data, and proof-of-concept efforts, while Text-to-SQL is best suited for ongoing, automated, and secure analytics — especially in collaborative, operational, or high-scale environments.

Below is a list of use cases.

## File Upload Scenarios

- **Individual Research**: Analysts exploring new datasets without IT involvement
- **External Data Analysis**: Working with third-party data outside your warehouse
- **Proof of Concepts**: Testing analytics approaches before formal implementation
- **One-Time Projects**: Analyses that don’t need repeating or updating

## Text-to-SQL Scenarios

- **Operational Dashboards**: Real-time KPIs and metrics are refreshed automatically
- **Cross-Functional Reporting**: Finance, marketing, and product teams accessing the same trusted data source
- **Customer-Facing Analytics**: Embedding data access in customer portals
- **Compliance Requirements**: Industries with strict data handling regulations
- **High-Volume Querying**: Organizations running hundreds of data queries daily

# Introducing Wren AI: Enterprise-Grade GenBI Solution

At Wren AI, we’ve built a specialized GenBI AI Agent that addresses the limitations of generic AI tools regarding enterprise data access. Our approach combines the intuitive experience of natural language with the power and precision of database querying.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f69334ffa79a7a6218_1*xEpUBmFbRy7g8dOnvs2AtA.png)

Using [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql) to operate data in tabular spreadsheets, charts, and SQL.

Wren AI stands apart with capabilities specifically designed for business data needs:

- **Schema-Aware Intelligence**: Understands your unique database structure, relationships, and business logic
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f62f1d9b02f7fefe13_1*qRe4QA3QP_iLxXIXwpt-0w.png)

Semantic modeling in [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql) allows LLM to understand your data structure and semantic relationships

- **Validation Pipeline**: Ensures generated SQL is correct and optimized before execution
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f6ca6e01e1df20c0cb_1*SUmIRLwXvsNGo5DYQprCnw.png)

Step-by-step reasoning about the thought process of SQL generation.

- **Governance Controls**: Fine-grained permissions and audit trails for sensitive data
- **Integration Ecosystem**: Connects with tools like databases, data warehouses, and business applications
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f8bbafe8194cf5ef4e_1*3lOFzzbxpjQjveT4kgvQIA.png)

[Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql) can connect to databases, files, and applications

- **Collaborative Features**: Share queries, insights, and visualizations securely across teams
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/682561f648f4f6f4b004efec_1*eKhPCF_Ts-U3_onvBUMq_Q.png)

Dashboard & Insights from [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql)

# How Wren AI Transforms Data Access Across Organizations

Using Wren AI, data teams gain efficiency and maintain control, business users get instant, intuitive access to insights, and leadership benefits from faster, more cost-effective decision-making. By streamlining workflows and democratizing data, Wren AI breaks down barriers to actionable intelligence.

For **Data Teams**:

- Eliminate report request backlogs
- Focus on high-value analytics instead of basic queries
- Maintain governance while democratizing data access

For **Business Users**:

- Get answers in seconds instead of days
- Ask follow-up questions naturally
- Make decisions with confidence in data freshness

For **Leadership**:

- Accelerate data-driven decision-making
- Reduce analytics bottlenecks
- The lower total cost of business intelligence

# Strategic Choices for Modern Data Teams

The ability to upload Excel files to ChatGPT and Claude represents an exciting step toward more accessible data analysis. However, for organizations serious about building a scalable, secure analytics foundation, Text-to-SQL solutions provide the necessary infrastructure for sustainable growth.

The future of business intelligence isn’t about choosing between ease-of-use and enterprise capabilities — it’s about bringing them together. Tools like Wren AI bridge this gap by making SQL invisible while keeping your data secure, fresh, and accessible to everyone who needs it.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/66fccdf15ed17ac19d0f1fed_0*dfda9zS6zqp0lg5E.png)

Make data accessable for everyone in your organization with [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql)

Instead of uploading yesterday’s data to get approximate answers, give your team the power to query today’s data for precise insights that drive business forward.

**Ready to transform how your organization accesses data? Request a demo or start your 14-day free trial at** [**getwren.ai**](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=file_sql)

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From Spreadsheets to GenBI Why Enterprise Analytics Needs Real-Time Database Access

Imagine this: it’s a regular Tuesday morning and your colleague **Bob** sends yet another email to your inbox:

> *Hi Alice,*

> *Which top 5 customers have spent the most in total orders, and what is their average order value?*

> *Best regards, Bob*

Bob’s questions are reasonable — he’s trying to make informed decisions. But every time you get one of these requests, it sets off a mini workflow: open your SQL editor, dig into the data warehouse, run a few queries, export the results, maybe make a quick chart, and then finally reply to the email. Multiply that by a few requests per week, and suddenly you’re spending a big chunk of your time just responding to metrics questions.

**Now imagine if you didn’t have to**

The Wren Engine MCP Server changes the game by enabling LLM like Claude to semantically understand and query your business data — using natural language. Thanks to the Model Context Protocol (MCP), you can build a complete, AI-driven workflow that takes an email like Bob’s, interprets it, queries the relevant data, and sends back a rich, formatted response — all automatically.

Better yet, this system seamlessly connects with Zapier’s workflow automation, acting as a bridge between incoming email requests and your data infrastructure. No more copying and pasting SQL. No more delay. Just answers — delivered.

With Wren Engine, your business intelligence process evolves from manual drudgery to fully automated insight delivery, allowing teams to focus on decision-making, not data wrangling.

# How Wren Engine Works

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea5a587c1e00dabc9482_1*FLPCKu4-vjrqC56fQEYyVA.png)

Image from [Fueling the Next Wave of AI Agents: Building the Foundation for Future MCP Clients and Enterprise Data Access](https://medium.com/wrenai/fueling-the-next-wave-of-ai-agents-building-the-foundation-for-future-mcp-clients-and-enterprise-332deada733e)

At the heart of the Wren Engine is a powerful idea: your business data shouldn’t just live in tables — it should live in context. Wren Engine is designed to give AI agents that context, allowing them to understand the relationships, logic, and meaning behind your data so they can answer business questions accurately and efficiently.

At the heart of the Wren Engine is a powerful idea: your business data shouldn’t just live in tables — it should live in context. Wren Engine is designed to give AI agents that context, allowing them to understand the relationships, logic, and meaning behind your data so they can answer business questions accurately and efficiently.

When someone asks a question — like Bob’s request about customer spending — Wren Engine help LLM interprets the intent using this semantic layer. It transforms natural language into a structured SQL query that’s tailored for your actual data sources. And because Wren supports multiple SQL dialects and integrates with modern query engines, that query gets executed with high performance and accuracy.

Once the results are in, Wren formats them into a clean, digestible response — often with tables, charts, or summaries — making it easy to share insights directly via email or internal tools.

You can find more technical details about the Wren Engine from [Jax’s excellent blog post here.](https://medium.com/wrenai/powering-semantic-sql-for-ai-agents-with-apache-datafusion-da963e69804f)

# Setting Up Wren Engine and Zapier MCP Servers

To start the Wren Engine MCP server, follow the detailed guide available [**QuickStart Wren Engine with MCP**](https://docs.getwren.ai/oss/engine/get_started/quickstart)

To use Zapier’s MCP service, you’ll first need to obtain your unique **MCP URL** and configure it in your preferred tool like Cursor, Claude Desktop, or any other platform that supports MCP.

## 1\. Get the Zapier MCP URL

Head over to your [Zapier MCP Settings](https://actions.zapier.com/mcp) page and copy your MCP URL. It will look something like this

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea59bc32f66ac68d8cf4_1*VepJyKT5A7CS7-EGYxkLxQ.png)

Paste this into your configuration file — for example, in `mcp.json` in Cursor or Cline Remote Servers — so that the platform knows how to communicate with the Zapier MCP server.

we use Cline for the following demo

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea5a5f1a50f8e6568d59_1*QY7e5FxVVaZ6AJs0quJgMQ.png)

## 2\. Configure the Actions You Want to Use

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea5919a4cd245220f65f_1*-jRNyePCrr1c05gZeXOAwg.png)

Back in the same Zapier MCP settings page, click the Edit MCP Actions button. From there, you can add specific actions (aka tools) you’d like to use — for example, if you want to interact with Gmail, you might choose an action like **Gmail: Find Email**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea595665bac8859d7dcf_1*Bi9kSSp7SMadnuu0oAavYw.png)

Each action is set up individually, so select the one you want, link your Gmail account if needed, and then click **“Enable Action.”**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea5a93e1bc968b9f9bdd_1*QIzZ39vGj1arGr5_Z0bB3Q.png)

## 3\. Start Using It!

Once the action is enabled, go back to your MCP interface and start using it with natural language. For example, you can say:

**“Find emails about Metrics Request”**

Zapier MCP will then trigger the appropriate tool action.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea5a80d10f18bf66ae99_1*oj3pxhxDryBU3BCR_ugH9A.png)

Just remember — Zapier MCP has rate limits (40 calls/hour), so try to make each request count!

# Connect the Dots

Now that everything’s wired up, let’s walk through how the pieces fit together — and why it’s a game-changer for your workflow.

## 1\. A business question arrives via email

Bob sends another one of his classic emails — but this time, you simply ask the agent

*Find the latest metrics request email in my Gmail and let me know which metrics are needed*

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea59006dc518a6e6ba84_1*L7fMUiwlyYagox9CcgooCg.png)

The agent really gets the answer to the metrics question:

**“Which customers have spent the most in total orders, and what is their average order value?”**

## 2\. The request is sent to Wren Engine via MCP

Again, let’s see what the agent can do for us.

**”Use Wren Engine to answer Which customers have spent the most in total orders, and what is their average order value?”**

The answer is drafted for review

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea59c341e38cf4da8ab3_1*W4XBg9BBKdH0uVD-xvvYVw.png)

## 3\. The answer is drafted for review

Instead of sending it immediately, lets **drafts an email** to review. You get the final say before hitting send — ensuring accuracy and adding any personal touch if needed.

just ask Agent **“draft the email for me”.**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6805ea590f8611ef4dc34e74_1*ZT1bAOaKvklYFRsopnnkuQ.png)

# Conclusion

This article showcases how integrating Wren Engine with Zapier through the Model Context Protocol (MCP) can automate and streamline business intelligence workflows.

By enabling natural language understanding of data requests, Wren Engine allows agents like Claude to interpret questions, query databases, and generate clear, structured responses. Combined with Zapier’s automation, email-based requests can be identified, processed, and drafted for reply — significantly reducing manual effort.

This setup empowers teams to focus on decision-making rather than data wrangling, while ensuring consistency and speed in delivering insights. With just a few configurations, your data operations become smarter, faster, and more collaborative.

# Wren Engine MCP Support Has Landed!

Wren Engine MCP integration is already underway — and now’s the perfect time to get involved! Explore Wren Engine’s power by connecting your own MCP client and experiencing semantic data querying firsthand.

🛠️ **Start building today:**  
[Our QuickStart with MCP guide](https://docs.getwren.ai/oss/engine/get_started/quickstart) walks you through everything from setup to deployment — whether you’re using parquet files or integrating with cloud sources.

📦 **Open source & evolving:**  
Wren Engine MCP is Apache 2.0 licensed and open to contributions. Check out our [GitHub repository](https://github.com/Canner/wren-engine) and see what’s under the hood.

👥 **Join the community:**  
Whether you’re here to build, explore, or contribute — [we’d love to have you](https://discord.com/invite/5DvshJqG8Z). Let’s shape the future of AI-powered data access together.

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Powering AI-driven workflows with Wren Engine and Zapier via the Model Context Protocol (MCP)

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Powering Semantic SQL for AI Agents with Apache DataFusion

In the evolving landscape of AI-powered data solutions, the temptation is strong to continuously refine and perfect core technologies like Text-to-SQL within Retrieval-Augmented Generation (RAG) architectures. After all, translating human language accurately into SQL queries is at the heart of empowering business users to interact directly with data. Yet, as crucial as Text-to-SQL is, it is becoming increasingly clear that focusing solely on incremental improvements in this technology may miss a more strategic opportunity: designing robust Feedback Loops and Memory Layers.

# Understanding the Basics of Text-to-SQL in RAG Architecture

Retrieval-Augmented Generation (RAG) combines retrieval of relevant context from databases or documents with generative AI models to provide precise and context-aware answers. The Text-to-SQL component within this architecture translates natural language questions directly into SQL queries, making data access seamless and intuitive for non-technical business users.

Wren AI, as detailed in our recent article [“How Uber is Saving 140,000 Hours Each Month Using Text-to-SQL and How You Can Harness the Same”](https://medium.com/wrenai/how-uber-is-saving-140-000-hours-each-month-using-text-to-sql-and-how-you-can-harness-the-same-fb4818ae4ea3), leverages cutting-edge models that effectively parse natural language questions into SQL, substantially reducing manual query writing and analytics bottlenecks. Uber’s use of similar technology demonstrates the enormous productivity gains achievable through effective Text-to-SQL applications.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67eb6894338fa6244d333a79_0*zys0X4Brm5iMIOKg.png)

[Wren AI Text-to-SQL](https://github.com/Canner/WrenAI) Architecture

However, as impressive as pure Text-to-SQL technology is, it alone is not enough to fully unlock the true potential of AI-driven analytics.

# The Limitations of Pure Text-to-SQL

While powerful, Text-to-SQL often encounters limitations:

- **Ambiguity in Natural Language:** Natural language is inherently ambiguous. Even advanced models can misinterpret user intent.
- **Lack of Contextual Memory:** Pure Text-to-SQL processes questions in isolation, without historical context, often leading to inconsistent or incorrect outputs when similar questions are posed differently.
- **Limited Learning from User Feedback:** Traditional implementations typically do not improve significantly over time as they fail to systematically incorporate user corrections and insights back into their model.

These limitations underscore the need to shift the strategic focus toward an improved Feedback Loop and Memory Layer.

# Why Feedback Loops and Memory Layers Are Critical

Feedback loops and memory layers fundamentally change how AI models interact with users and data. They allow systems to learn continuously from user interactions, context, and prior knowledge, significantly enhancing overall accuracy and reliability.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67eb689432151011582cf650_1*iXEy1hP72XSAhQJu06TjQw.png)

Text-to-SQL feedback loop and memory layers

At Wren AI, our GenBI AI Agent, we’ve introduced powerful new features that prioritize these essential aspects, moving beyond the pure Text-to-SQL approach:

# Question-SQL Pairs: Building a Dynamic Knowledge Base

Question-SQL pairs are foundational to creating a smarter, adaptive AI. By mapping natural language questions directly to validated SQL queries, Wren AI forms a continuously growing knowledge base. When a user poses a similar query, the system can leverage existing, validated SQL queries, dramatically increasing the accuracy and consistency of outputs.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67eb689434a58f7dc2a5924c_0*gTB4F4aOKZNwESYI.png)

Question-SQL pair in [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=feedback)

**Techniques for Effective Question-SQL Pairing:**

- **Similarity Clustering:** Grouping similar questions and queries to enhance retrieval effectiveness.
- **Active Learning (Work in Progress):** Encouraging users to validate and correct SQL queries, creating a virtuous cycle of improvement.
- **Version Control (Future Planning):** Managing different versions of queries, allowing rollback or comparison, and enhancing transparency and trust.

# Introducing Instructions: Enhancing Contextual Accuracy

Wren AI now supports a powerful “Instructions” feature, redesigning how it understands and processes user intent:

- **Global Instructions:** Users define overarching rules that guide Wren AI’s interpretation of data models and business logic. This ensures broad alignment with organizational standards and data governance.
- **Question-Matching Instructions:** These targeted instructions trigger based on specific query types or contexts, delivering highly accurate SQL generation tailored precisely to business scenarios. This feature allows companies to encode institutional knowledge directly into the AI’s workflow.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67eb68940bd43839904c502c_0*nmrmbi4pyX2WXVL8.png)

Instructions in [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=feedback)

These Instructions significantly boost the precision of SQL generation, reducing the need for manual query adjustments and accelerating insight delivery.

# Transparency and User Trust: Visibility Into the SQL Generation Process

One critical barrier to wider adoption of AI-powered analytics has been the perceived “black-box” nature of AI decisions. To address this, Wren AI emphasizes transparency:

- Users gain complete insight into why specific database tables are selected.
- Clear explanations of how questions are interpreted.
- Detailed breakdown of SQL query construction.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67eb6893cf9bb6466cad6fb3_1*DgKPaEGEYZr_jyrF6Hy2xA.png)

SQL generation transparency in [Wren AI](https://getwren.ai/?utm_source=blog&utm_medium=post&utm_campaign=feedback)

This transparency builds trust and empowers users to refine queries proactively. With the ability to edit and feed corrections back into Wren AI, users participate directly in improving system intelligence and responsiveness.

# Building a Robust Feedback Loop: Continuous Improvement in Action

The power of a strong Feedback Loop and Memory Layer lies in iterative, continuous improvement:

- **Immediate Feedback Integration:** Real-time query adjustments from user interactions immediately improve future query accuracy.
- **Long-term Contextual Memory:** Historical interactions inform future queries, allowing Wren AI to proactively anticipate user needs based on past behavior.
- **Collaborative Intelligence:** Users actively teach Wren AI, creating a genuinely collaborative human-AI partnership.

By incorporating these advanced feedback loops, Wren AI transcends typical Text-to-SQL limitations, becoming a genuinely adaptive AI partner for your business.

# Prioritizing Feedback and Memory for Lasting Impact

The future of AI-driven analytics isn’t merely about incrementally refining technical capabilities like Text-to-SQL. It’s about creating systems capable of continuously learning, adapting, and integrating user feedback into their decision-making processes. Wren AI’s emphasis on robust Feedback Loops and Memory Layers ensures that organizations not only leverage existing data efficiently but also strategically build and retain institutional knowledge.

Through innovative features like Question-SQL Pairs, targeted Instructions, and transparency in the AI decision-making process, Wren AI empowers organizations to achieve sustained accuracy, consistent query reliability, and deeper insights.

Ultimately, by prioritizing Feedback Loops and Memory Design over incremental technical enhancements alone, Wren AI offers a more powerful, sustainable approach — ensuring businesses stay ahead in a rapidly evolving AI-driven world.

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Beyond Text-to-SQL: Why Feedback Loops and Memory Layers Are the Future of GenBI

In the fast-moving world of artificial intelligence, new standards and technologies emerge frequently. Yet few have sparked as much excitement and grassroots innovation as the **Model Context Protocol (MCP)**. Designed as an open standard for connecting Large Language Models (LLMs) with tools, databases, and other systems, MCP is becoming a foundational pillar in the age of AI agents. Although there are many impressive MCP demos and innovative applications emerging every day, we rarely see enterprises adopting these tools as part of their daily workflows.

For MCP to reach its full potential — especially in the enterprise — it must go beyond web automation or local file interactions. **Without the right data, MCP remains very limited for enterprise use cases**. Once we solve this critical challenge, we believe we’ll start seeing explosive new growth in AI adoption. At Wren AI, we see enabling AI to query the right data with full business context as essential — and that’s precisely where the Wren Engine comes in.

# The Rise of MCP: Innovation Across the Community

The Model Context Protocol is a standardized, open framework that allows AI models to communicate with external services in a consistent, secure, and extensible way. It defines a common language for passing context, making it possible for different tools and services to interoperate with AI agents without reinventing the wheel each time.

In just a few months, thousands of MCP servers have been developed and shared within the community. These servers empower AI systems to interact with local applications, web services, cloud storage, and APIs through natural language prompts. There are a lot of great materials available on the internet showcasing various MCP demos and implementations.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67e38e28f31b6a05ec054f5a_1*gth-B3mUM8oONUHyExdTFg.png)

By the time of 2025/3, there are about 4405 MCP servers on [https://mcp.so/](https://mcp.so/)

MCP is built on a simple but powerful client-server model. An **MCP client** is typically the AI assistant or LLM interface that initiates a request — such as asking a question, generating content, or executing a task. The **MCP server** is the responder that executes that request by connecting to a specific tool or data source and returning the structured result.

Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect devices to a wide range of peripherals and accessories, MCP offers a standardized way to connect AI models to various tools, databases, and external services. This standardization allows modular and scalable AI workflows across different environments and technology stacks.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67e38e271c0673eb61c90553_1*mNVG-UQ7zPYIN3P9_SX2Sw.png)

General Architecture of MCP from [MCP official site](https://modelcontextprotocol.io/introduction)

# A Glimpse into Today’s MCP Use Cases

- **AI Personal Assistants**: Imagine querying your calendar, browsing local files, or updating notes — all via natural language. MCP projects have made this seamless.
- **Code Repository Management**: Developers have built MCP integrations with GitHub, enabling AI to browse repositories, comment on code, and suggest fixes autonomously.
- **Web Automation Agents**: Agents powered by MCP can now automate repetitive web tasks like checking prices, booking meetings, or scraping reports.

These are impressive demonstrations of what’s possible with AI agents. But most of these solutions are still centered around individual workflows — running on local machines, handling personal data, or automating tasks in isolated environments.

# The Enterprise Gap: When Data Matters More

At the enterprise level, the stakes — and the complexity — are much higher. Businesses run on structured data stored in cloud warehouses, relational databases, and secure filesystems. From BI dashboards to CRM updates and compliance workflows, AI must not only execute commands but also **understand and retrieve the right data, with precision and in context**.

While many community and official MCP servers already support connections to major databases like **PostgreSQL, MySQL, SQL Server**, and more, there’s a problem: **raw access to data isn’t enough**.

Enterprises need:

- **Accurate semantic understanding** of their data models
- **Trusted calculations and aggregations** in reporting
- **Clarity on business terms**, like “active customer,” “net revenue,” or “churn rate”
- **User-based permissions** and access control

Natural language alone isn’t enough to drive complex workflows across enterprise data systems. You need a layer that interprets intent, maps it to the correct data, applies calculations accurately, and ensures security.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67e38e27eb58c632d8399bd4_1*nO95FdXSraHGDW88lJE55g.png)

Today’s MCP servers connect to databases.

This is where most MCP database integrations today fall short.

# Why Semantic Layers Are the Missing Link

The current generation of AI assistants excels at language — but falters when it comes to **understanding your business**. Without a semantic layer that defines how data is connected, what metrics mean, and how to perform consistent aggregations, AI is left guessing.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67e38e273ba8b6c4bd7376e3_0*ydh7bVwY3l8y_4Jn.png)

For example, when a user asks, “What’s our revenue in Q4 for new customers in EMEA?”, the system must:

- Know what “revenue” means in the business context
- Apply the correct time filters
- Join customer and sales data appropriately
- Recognize “new customers” as a derived attribute

Doing this accurately and consistently across teams is impossible without a semantic foundation. That’s why we built the **Wren Engine**.

# Introducing [Wren Engine](https://github.com/Canner/wren-engine): The Semantic Engine Powering the Semantic Layer for Future MCP Clients and AI Agents

The [Wren Engine](https://github.com/Canner/wren-engine) is an [open-source](https://github.com/Canner/wren-engine) semantic engine powering the semantic layer for AI-driven data access. ***This powerful engine is designed to enable hundreds and potentially thousands of future MCP clients to retrieve the right data from their databases seamlessly and accurately.*** By building the semantic layer directly into MCP clients, such as Claude, Cline, Cursor, etc. Wren Engine empowers AI agents with precise business context and ensures accurate data interactions across diverse enterprise environments.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67e38e287652e89f9618d2a7_1*FwmL3Z0vU_8PETe-msw9bQ.png)

The Future of MCP Ecosystem with Wren Engine

## Key Capabilities of the Wren Engine

**1\. Semantic Modeling**  
Wren Engine enables you to define your business logic, relationships, metrics, and KPIs in a structured, graph-based format. This semantic model maps your natural language requests to the correct data with precision.

**2\. Real-Time SQL Rewrite**  
Using metadata and user context, Wren Engine rewrites AI-generated SQL in real time — applying necessary joins, filters, calculations, and access controls.

**3\. Business Context Awareness**  
Rather than relying on fragile prompt engineering, Wren Engine gives AI agents a robust layer of understanding about your business: what “net revenue” means, how a “customer cohort” is defined, and which segments apply to which teams.

**4\. Native Support for Major Databases**  
Whether you use PostgreSQL, MySQL, Snowflake, or Microsoft SQL Server, Wren Engine integrates easily and maintains high performance.

**5\. Secure Role-Based Access**  
It handles authentication and data visibility based on user roles, so queries respect enterprise-level governance requirements.

# Driving Enterprise Adoption: From LLM Playground to AI-Powered BI

As AI adoption accelerates in the enterprise, teams are experimenting with LLMs in all kinds of workflows — from sales support to operations. However, many of these experiments stall when AI can’t answer basic questions like:

- “What’s our MRR last month by region?”
- “Which customers have churned in the last 60 days?”
- “Show me marketing leads that converted to deals this quarter.”

It’s not that AI can’t generate SQL. It’s that without a semantic layer, **it doesn’t know what to query**. And that limits its ability to create value.

By deploying Wren Engine-powered MCP servers, enterprises can:

- Empower teams to ask business questions in natural language
- Generate trusted, explainable insights instantly
- Maintain consistency across data definitions and dashboards
- Scale AI usage securely across departments

This is how we move from individual proof-of-concepts to **enterprise-grade productivity**.

# Endless Possibilities and Imagination with [Wren Engine](https://github.com/Canner/wren-engine) and MCP Servers

To better understand the impact, let’s look at how Wren Engine unlocks value by connecting with various powerful MCP clients in real-world enterprise workflows:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67e38e284aab7b939d9d4fde_0*W6ZMFxo73ClNywIe.png)

## Preplexity + Wren Engine: Smart Knowledge Retrieval

**Task:** An analyst wants a reliable answer backed by company data and external sources.

**With Wren Engine:** Preplexity MCP retrieves answers based on both internal knowledge base and connected database via Wren Engine — ensuring the insight is accurate and grounded in company context.

## HubSpot + Wren Engine: Intelligent CRM Updates

**Task:** A marketing ops manager wants to find and update leads in the CRM.

**With Wren Engine:** Ask “Update lifecycle stage for leads from last campaign with no engagement” — HubSpot MCP pulls CRM records and Wren Engine powers the logic behind filters and updates.

## Zapier + Wren Engine: Workflow Automation

**Task:** Automate a report summary each time a sales deal closes.

**With Wren Engine:** Zapier MCP triggers the pipeline, and Wren Engine queries the semantic data model to generate deal summaries based on contextual metrics.

## Google Docs + Wren Engine: AI-Powered Document Search

**Task:** A legal team member needs to find contracts expiring this quarter.

**With Wren Engine:** Google Docs MCP accesses document storage and Wren Engine identifies relevant files based on semantic understanding of metadata and content.

# What’s Next: Building the Semantic Future of AI

We’re entering a new era of generative AI, where language is the interface — but **data is the fuel**. For AI to fulfill its promise in the enterprise, it must speak the language of business data fluently, securely, and accurately.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fd53142159a4cda575_0*_gjE0eC2DVaOojbn.png)

The Interface for MCP Clients to any databases

At Wren AI, we believe the most powerful AI systems won’t just generate words — they’ll generate truth. That means making sure every chart, metric, and insight is rooted in **semantic accuracy**.

By combining MCP’s extensibility with the semantic intelligence of Wren Engine, we’re creating a new standard for how businesses interact with data using AI.

MCP is one of the most exciting developments in the open AI ecosystem today. It empowers the community to rapidly build integrations that make LLMs more useful, capable, and dynamic.

But for enterprise adoption to truly take off, we must solve the **hard problem of data context** — ensuring that AI doesn’t just say things confidently, but says things **correctly**.

That’s the mission of Wren Engine.

If you’re building MCP-powered agents or exploring how generative BI can transform your company, let’s talk. Your data already holds the answers — Wren Engine just helps your AI find them.

# **Wren Engine is Fully Open Source!**

Explore Wren Engine’s capabilities firsthand by visiting 🔗[our GitHub repository](https://github.com/Canner/wren-engine), license under Apache 2.0.

Try it yourself by connecting your MCP clients to Wren Engine and witness the magic of semantic data access. The Wren Engine MCP server is still in its early phase, and we are actively improving it. We warmly invite you to join our community and help accelerate the future of MCP.

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Fueling the Next Wave of AI Agents Building the Foundation for Future MCP Clients and Enterprise Data Access

In today’s fast-paced business environment, data is your most valuable asset. However, effectively managing and operating your database or data warehouse often requires advanced SQL expertise. Wren AI Cloud introduces a simplified approach: **a spreadsheet-like management interface** directly on top of your database, enabling users to manipulate and analyze data using natural language effortlessly — no SQL required.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6b7e57b56089b8c5e0_0*xJN7ZxlKA2BPRFS1.png)

AI enriches data based on your row information

## Why an AI-powered spreadsheet-like Interface Matters for Modern Businesses

Traditionally, businesses manage data through databases, requiring SQL expertise to retrieve, cleanse, transform, filter, and group data. Users often depend on technical teams for even simple data queries, leading to delays and inefficiencies.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6bdd6451338025810f_1*Vc4FIvODE7DbSaBa0nfqWQ.png)

Business requests rely on the data team to write SQL and retrieve from data silos.

The introduction of an AI-powered spreadsheet-like interface directly addresses these issues. Unlike conventional spreadsheets, this innovative solution serves as a powerful tabular visualization layer directly connected to your databases. It leverages LLM using natural language, allowing users — regardless of their technical background — to intuitively operate and manipulate data without writing or understanding SQL. Here are some examples below.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6c1bdedb88617107a6_1*iS2rWRIC0d_p8Uk5eB4fLQ.png)

Use natural language to filter out values

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6b11e82f431d916491_1*_m7zU5d35QZwKwuxfmwzqA.png)

Use natural langauge to group categories

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6b3fceaf577de8ecd8_1*aJ0Ny8VbL0eCO-xok3OqMA.png)

Enriching data from other datasets, AI will map ID based on semantic relationships in the semantic modeling.

## Challenges Faced Without an AI-Powered Data Interface

Before this innovation, businesses confronted several persistent obstacles:

### 1\. Dependence on Technical Teams

Data retrieval and manipulation typically require database administrators or analysts proficient in SQL, creating bottlenecks.

### 2\. Complexity of SQL Queries

Even minor data tasks require complicated SQL queries, limiting accessibility for non-technical staff.

### 3\. Delays in Data Insights

Relying on technical experts means longer waiting periods, delaying crucial insights and decision-making.

### 4\. High Potential for Errors

Complex manual SQL operations can introduce errors, compromising data integrity and decision reliability.

## Introducing Wren AI Cloud’s Spreadsheet-Like Interface for Databases

Wren AI Cloud transforms how your team interacts with database-stored data. Our interface mirrors the simplicity and familiarity of spreadsheets. This is powered by sophisticated SQL queries running invisibly beneath the surface. Users can effortlessly filter, cleanse, group, and enrich database data through natural language commands without ever writing SQL.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92f2968a0fcfe84960930_0_N5P8Og2Vkrn_Hocx.gif)

## Benefits of Using Wren AI Cloud’s Interface

### 1\. Intuitive Natural Language Operation

Simply enter commands like, “Show contacts from last quarter” or “Group sales data by region and sum revenue,” and Wren AI Cloud automatically translates your request into SQL queries behind the scenes.

### 2\. Seamless Database Connectivity

**Operate directly on your database tables** without data duplication or manual transfers. Wren AI Cloud provides real-time data interactions through a spreadsheet-like visualization.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6c7233737408ead0c1_1*zf-FoNApb8_Oj9ZPKJ5tcA.png)

You can select from a model (table) from your database, or just create from a SQL.

### 3\. Instant Data Transformation and Cleansing

Tasks such as removing duplicates, filtering out incomplete records, and aggregating data happen instantly with simple natural language inputs.

### 4\. Real-time Data Updates

Every operation you perform generates dynamic SQL queries, ensuring your visualization always reflects the latest available data.

### 5\. Empowering Non-Technical Users

Users across your organization can independently explore and manipulate data, fostering greater productivity, agility, and informed decision-making.

## Practical Use Cases for Wren AI Cloud’s Spreadsheet-Like Interface

Wren AI Cloud’s innovative interface provides significant advantages in several business scenarios, such as below:

### 1\. Marketing and Sales Optimization

- Effortlessly segment database-stored customer records to build targeted marketing campaigns.
- Quickly cleanse lead databases and apply lead-scoring algorithms via natural language commands.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6b574fd84b91bb79a3_1*alzSP4W9rZ94MB0mYrIWbQ.png)

### 2\. Financial Reporting and Analysis

- Rapidly perform monthly or quarterly financial summaries by simply entering intuitive natural language requests.
- Automate complex compliance-driven data cleansing and report generation tasks.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6bc47b3e4754d364aa_0*MgZD7ZAEDmr__RD9.png)

### 3\. Inventory and Supply Chain Management

- Instantly group and summarize inventory levels, reorder points, and supplier performance directly from your database.
- Quickly identify and address inventory discrepancies through intuitive filtering commands.

### 4\. Human Resources Analytics

- Easily transform employee data for payroll, performance reviews, or compliance audits without manual SQL intervention.
- Filter and group employee data effortlessly, improving HR processes and accuracy.

## Leveraging HubSpot Data with Wren AI Cloud’s Interface

With Wren AI Cloud HubSpot data boilerplate, you can easily onboard your HubSpot data with a click; in this example, we’ll use the contact data in your HubSpot dataset to operate with Wren AI Cloud AI-powered Spreadsheet. Without needing SQL expertise, here’s how Wren AI Cloud’s spreadsheet-like interface revolutionizes your data operations:

- **Natural Language Filtering**: Simply type “*Exclude contacts using gmail and outlook*” and Wren AI Cloud automatically generates the required SQL to filter out incomplete entries.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6b8b33f97e4a8f4bab_1*SNtwMTF1Wxve1-4nhFtnkQ.png)

Just use natural language and describe

- **Dynamic Lead Scoring**: Enter, “*Calculate lead score based on engagement level and conversion history*,” prompting Wren AI Cloud to generate advanced SQL queries that instantly compute lead scores.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6ac8bfb218cc349171_1*mWKcQ0YkcpxpPb0zUbPUXQ.png)

You can define your lead score, AI will do the rest

- **Data Ordering and Prioritization**: Enter intuitive commands like “Sort contacts by lead score descending,” and your data is instantly reordered, enabling efficient prioritization.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67d92e6b044856b1ed6db0e5_1*AQ_BDemx4r0xQpFA-vzWQw.png)

Descending and Ascending sort

- **Data Enrichment via Natural Language**: Issue commands such as “Enrich contact data with company information,” and Wren AI Cloud generates SQL queries that seamlessly integrate external enrichment data.

After these operations, simply save your configured visualization. Every time you revisit, your data visualization layer dynamically reflects the latest data directly from your database, with all SQL operations automatically handled.

## Final Thoughts

Wren AI Cloud’s AI-powered, spreadsheet-like interface fundamentally transforms how your business interacts with data. By combining intuitive, natural language commands with powerful, automated SQL query generation, your organization gains unprecedented data agility, speed, and accuracy.

Experience the next generation of data interaction with Wren AI Cloud and elevate your data-driven decision-making today.

Learn more at [https://getwren.ai/](https://getwren.ai/).

Join us as we push the boundaries of business intelligence with GenBI.

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Transform Your Database Management with an AI-powered spreadsheet-like Interface

Data is a critical asset in today’s fast-paced business environment. Yet, wrangling that data into a usable form for many teams can feel like a never-ending challenge. Enter Wren AI’s latest feature release: **AI-Powered Spreadsheets and Pre-built Cross Platform Metrics**. Designed to simplify how you connect, clean, and analyze your data, our new solution puts the power of artificial intelligence at your fingertips — no coding required. Additionally, the pre-built metrics feature allows users to quickly implement standardized measurements and insights, enhancing the overall data analysis experience.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fd53142159a4cda575_0*_gjE0eC2DVaOojbn.png)

Supercharge Your Insights with GenBI & AI-Powered Spreadsheets

A common challenge for any growing organization is measuring success consistently across multiple tools. That’s why Wren AI has introduced an extensive library of Pre-Built Cross-Platform Metrics — **over 100 curated formulas that calculate** popular KPIs used in sales, marketing, e-commerce, customer success, and more.

Bring your database, warehouse, or SaaS tool into a tabular format, and then use natural language commands to cleanse, filter, or enrich that dataset within the spreadsheet. This approach makes data manipulation highly approachable to non-technical users while keeping the process straightforward and error-free.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/676427fc728e95c59c722319_0*XtxrgcRkG67Ifxtl.png)

# **Why We Built AI-Powered Spreadsheets**

Data often exists across various systems: CRM tools, e-commerce platforms, custom databases, and more. Most platforms offer some level of built-in reporting, but these are usually siloed. For deeper insights, teams export CSVs, wrangle data in spreadsheets, or rely on specialized data engineers to write SQL queries or macros.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fdcf681db7a6182bbf_1*cusYTTJ33qj2UXFcZl3U3w.png)

Wren AI — AI-powered Spreadsheets

This situation creates friction and slows down decision-making. At Wren AI, we wanted to develop **AI-powered Spreadsheets** to give non-technical users a way to directly **access, clean, and analyze** data without advanced query languages. By focusing on natural language operations and an intuitive interface, we aim to bring data empowerment to everyone in the organization, from the executives to the product and marketing teams.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fecbf559dfc99b89e3_1*A3uJA8gHHaZz4UxdHrKpXQ.png)

Enrich your data through AI.

## **Simple Connectivity to Your Preferred Data Source**

The first step in using our **AI-powered Spreadsheets** is connecting to a data source. We’ve simplified the process so that you can link a database or a SaaS tool in just a few clicks. Popular choices include:

- **Relational databases** (MySQL, PostgreSQL, SQL Server, and more)
- **Cloud data warehouses** (Snowflake, BigQuery)
- **SaaS applications** (Facebook Ads, HubSpot, Google Analytics, Woocommerce)
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fecbf559dfc99b89e0_1*Rh8t0v1jpzpCUlxOJFlLWQ.png)

Various of integrations for your workflow.

Once you select a single data source and authenticate it, you’ll view that data in a clean, tabular format within our AI-Powered Spreadsheet. From there, you can immediately interact with the data using natural language — transforming columns, filtering rows, or enriching fields. Wren AI executes all changes, ensuring accurate, real-time updates without the need for coding.

## **Natural Language Cleansing, Filtering, and Enrichment — Direct on Your Data Sources without moving!**

**Data cleaning** is often the most time-consuming part of any data project. Traditional spreadsheet users rely on formulas or macros, and teams handling large datasets often need specialized technical knowledge (like SQL or Python). Our AI-Powered Spreadsheets **cut through that complexity** by enabling you to type instructions in plain English (or your preferred language). For instance:

- “Remove all rows where the email column is empty.”
- “Split the ‘Full Name’ column into ‘First Name’ and ‘Last Name’ columns.”
- “Convert the purchase date column to ‘YYYY-MM-DD’ format.”
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fd7ef445a83d95d115_1*uXuyVEHCMq2MenORU_i0_w.gif)

Cleaning, standardizing and enrich your data through AI.

These commands are interpreted and executed by [Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=spreadsheets), transforming your dataset instantly. You don’t need to memorize complex formulas or switch between multiple tools. The result is a **smooth, intuitive workflow** that saves time and reduces the risk of error.

*Note that while you can connect multiple data sources to Wren AI overall, each AI-powered spreadsheet is tied to* ***a single dataset*** *at a time. You’ll work with that dataset’s columns and rows, applying transformations without crossing into other data repositories.*

## **Even more powerful with** [**GenBI**](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=spreadsheets)**: Query Data & Visualize in Seconds**

If natural language cleaning helps you prepare data, combining Wren AI’s GenBI can be more powerful than ever. You can now clean and model your data through AI-powered spreadsheets, allowing you to ask your data questions and see the results in an immediate, visual format. Think of AI-powered spreadsheets plus GenBI as your personal data engineer and analyst's assistant — one that doesn’t require a single line of code.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fe4354145f8fba5a9f_1*PgB_hI878dcOZrYXlDh0LQ.png)

Build visualization charts and metrics in seconds

**How GenBI Works:**

1. **Just Ask Your Question**: Type or speak something like, “How many new leads were created last month?”
2. **Instant Data Retrieval**: GenBI automatically fetches the relevant rows and columns from the connected data source.
3. **One-Click Chart Generation**: With a single click, you can transform the query result into a line chart, bar chart, or another visualization.
4. **Pin to Dashboard**: Found something worth monitoring over time? Pin that chart straight to your Wren AI dashboard so you can revisit it whenever you need — no re-query required.

GenBI’s natural language processing enables you to query data in ways that feel human — such as asking for the “top five product categories by revenue” or the “number of leads by region.” It’s the ideal companion for quick, ad-hoc analysis, liberating you from the burden of writing scripts or repeatedly creating pivot tables.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fdcf681db7a6182bae_0*09Rq1TdduW0Gyju8.png)

Build dashboard and metrics from your data

# **Pre-Built Cross-Platform Metrics: Unified KPIs Made Easy**

Building a unified dashboard from scratch can be daunting, especially when your data is spread across multiple SaaS tools. That’s why we’ve introduced **Pre-Built Cross-Platform Metrics** — over 100 handpicked KPIs curated by the Wren AI team to give you **instant, actionable insights**.

## **Ready-Made KPI Library**

Our growing library covers an ever-expanding range of popular platforms, including HubSpot, WooCommerce, Stripe, and more. Each metric has been carefully defined and tested to ensure you’re following industry standards for tracking performance.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fdcf681db7a6182bb7_1*0kFzYmEVn3CprlM6SI5sLA.png)

Pre-built Metrics from Stripe

From lead conversion rates in HubSpot to average order value in WooCommerce, you’ll find the KPIs you need right out of the box — without building complex formulas or hunting for data points yourself.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67bec8fecbf559dfc99b8a1e_1*O5Mj0_HlDh493y4PcLaFng.png)

Pre-built Metrics from HubSpot

## **Simple Dashboard Creation**

Once you connect your tools to Wren AI, you can **browse and select** any of these pre-built metrics, instantly populating your dashboards with up-to-date figures. Whether you’re measuring churn in Stripe or monitoring signups via HubSpot, our metrics library gives you a single pane of glass to see how all parts of your business are performing — no manual data merges required.

## **Consistency & Accuracy**

By standardizing how each KPI is calculated, we ensure every department in your organization is speaking the same “metrics language.” That translates into quicker decisions, clearer communication, and a more cohesive understanding of where your business stands.

With **Pre-Built Cross-Platform Metrics**, you’re freed from repetitive setup tasks and empowered to focus on what truly matters: **turning real-time data into proactive strategies for growth**.

# **Getting Started with the AI-powered Analytics Features**

Ready to experience Wren AI’s **AI-Powered Spreadsheets**, **GenBI**, and our **Pre-Built Cross-Platform Metrics**? Follow the steps below to see how quickly you can transform raw data into actionable insights:

## 1\. **Sign Up or Log In**

If you’re new to [Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=spreadsheets), head over to our website to create your account. Existing users will find the latest features already enabled in their dashboards.

## 2\. **Connect Your Data Source**

Select a single data source — like HubSpot, Stripe, or a relational database — and authorize it with Wren AI. Within moments, your data will appear in a clear, tabular view.

## 3\. **Explore AI-Powered Tools**

Use natural language to **clean, filter, or enrich** your dataset. Then, try out **GenBI** to query your data in plain English, generate charts with a single click, and pin those visualizations to your dashboard.

## 4\. **Activate Pre-Built Metrics**

Browse our **library of 100+ KPI formulas**, designed specifically for popular SaaS platforms (e.g., WooCommerce, HubSpot). Pick the metrics you want and see real-time insights emerge — no complex setup required.

## 5\. **Generate Dashboards for Routine Use**

Build reusable dashboards by combining your pinned charts and pre-built metrics in one place. Monitor performance over time, compare historical data, and share the dashboards with your team for seamless collaboration.

# **Join Us on This Data Journey**

Next-generation business intelligence — GenBI and AI-powered Spreadsheets

We are incredibly excited to unveil a significant milestone in our journey: the launch of AI-Powered Spreadsheets! This innovative release combines the power of natural language data cleansing, rapid querying and visualization through GenBI, and an expanding library of Pre-Built Cross-Platform Metrics.

Wren AI empowers you to harness your data like never before, igniting your productivity and insights. Embrace this new era of data analysis and supercharge your decision-making with GenBI and AI-Powered Spreadsheets!

‍

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Introducing Wren AI’s New AI-Powered Spreadsheets & Pre-built 100+ Cross-Platform Metrics

At Wren AI, we’ve spent months rethinking what Business Intelligence (BI) can — and should — be. Traditional BI has long served as the workhorse for data reporting and analysis, but it’s starting to feel like we’re trying to fit modern problems into an outdated framework. That’s why we’re so excited about GenBI — **Generative Business Intelligence** — which reimagines BI with advanced AI, natural language, and a workflow that learns and evolves just like a human team member.

In this post, I’d like to share our journey from how we think about the transition from traditional BI to GenBI and why the future belongs to solutions that are as flexible and intuitive as the people using them. I’ll focus on five key design principles that set GenBI apart:

- The power of the semantic layer
- The promise of an agentic layer
- Keeping humans in the loop
- An agentic workflow that truly collaborates
- A conversational interface that speaks your language

Let’s dive in.

# **Rethinking BI: The Limitations of Traditional Approaches**

Traditional BI systems have been a mainstay for decades. They rely on static dashboards and pre-built reports, and they require specialized know-how to extract actionable insights. In many organizations, you still see data locked behind rigid schemas and endless spreadsheets. Sure, they work for routine reporting, but when the business environment changes rapidly, these systems can’t keep up.

The shortcomings are clear:

- **Inflexibility:** The underlying data models are often static, making it hard to respond quickly to new business questions.
- **Complexity for non-experts:** Not everyone has the training to build or interpret complex reports.
- **Slow evolution:** Creating new reports or updating dashboards can take days if not weeks.

GenBI is designed to break these barriers by being more agile, accessible, and, importantly, more human.

# **Introducing GenBI: A New Paradigm in Business Intelligence**

Generative Business Intelligence, or GenBI, represents a radical shift in how organizations harness data for decision-making. Unlike traditional BI — which depends on static dashboards, predefined queries, and manual report generation — GenBI leverages advanced artificial intelligence, natural language processing, and proactive workflows to create an adaptive and interactive data ecosystem.

## **What is GenBI?**

GenBI is designed to make data work smarter and faster. It combines real-time data processing with generative AI capabilities that not only analyze past trends but also predict future outcomes. This approach turns data into a dynamic, living asset that continually refines itself through ongoing user interactions. In essence, GenBI acts as a digital data analyst: it understands your business context, anticipates needs, and actively supports decision-making by suggesting or even initiating actions when necessary.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b09245afe5597f58e5_0*OMyGedTA04K82ul4.png)

The four foundational layers of GenBI, [learn more check post here](https://getwren.ai/post/the-future-of-business-intelligence-the-generative-bi-genbi?utm_source=web&utm_medium=landing&utm_campaign=genbi)

## **Design Differences: GenBI vs. Traditional BI**

The transition from traditional BI to GenBI is as much about design philosophy as it is about technology. Here are the key design differences:

***Flexibility and Adaptability:***

Traditional BI systems often operate on rigid, predefined schemas that require manual updates. GenBI, on the other hand, is built to adapt on the fly — integrating new data sources and evolving in response to changing business questions without significant reengineering.

***Conversational Interfaces:***

GenBI empowers users by enabling them to interact with their data in plain language. Instead of navigating through complex dashboards and learning technical query languages, users can simply ask questions as they would in a natural conversation, making data insights accessible to non-technical stakeholders.

***Proactive, Agentic Workflows:***

Rather than being a passive repository of reports, GenBI actively monitors key performance indicators. Its AI agents function like human data analysts — interpreting ambiguous queries, seeking clarifications, and even suggesting actions to bridge the gap between insight and implementation. This proactive workflow transforms BI from a reactive tool into a dynamic business partner.

***Human-Like Intelligence with Memory:***

One of the most compelling features of GenBI is its memory layer. Similar to how human analysts rely on past experiences to inform their decisions, GenBI remembers historical interactions and feedback, using this context to refine its analysis and recommendations over time. This iterative learning process makes the system increasingly accurate and aligned with your organizational needs.

***Seamless Integration and Holistic Context:***

GenBI’s robust semantic layer creates a living map of your organization’s data — capturing relationships, context, and domain knowledge in a way that traditional BI rarely achieves. This holistic understanding allows the system to generate insights that are both deeper and more relevant.

By reimagining the core architecture of business intelligence, GenBI isn’t just an upgrade — it’s a reinvention of how data drives business decisions. In the following sections, **we’ll explore how the semantic layer, agentic workflow, and other design elements of GenBI work together to deliver this transformative experience.**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67adf6d88c578657d11fc4f8_0*ZY4lWhS3O0A8TS7l.png)

Wren AI — GenBI Interface

# **The Semantic Layer: Giving Data Real Meaning**

Imagine trying to answer a business question when you have to first decipher a jumbled mess of technical jargon. That’s where the semantic layer comes in. In traditional BI, it might simply rename “cust\_id” to “Customer ID.” In GenBI, however, the semantic layer is so much more. It’s a living map of your organization’s data, weaving together relationships, context, and domain knowledge.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/66c41befc04f13f217ce1d5e_1*xunAEpVzfkvmHMBLvuIqxw.png)

This enriched layer lets the system understand complex queries naturally. Ask, “Which customer segment drove our best quarterly growth?” and GenBI will connect the dots between customer behavior, sales data, and market trends. Every interaction with the system refines this knowledge, making your data smarter over time. At Wren AI, we’ve invested heavily in creating a semantic layer that learns from every query, ensuring that your insights get sharper with each use.

# **Human-in-the-Loop: Merging Machine Precision with Human Insight**

No matter how smart an AI gets, there’s still nothing like human intuition. In a GenBI system, automation is powerful, but we never lose sight of the value of human oversight. The AI handles repetitive tasks and suggests actions, but real people — armed with domain expertise and strategic vision — make the final call.

There are several reasons why keeping humans in the loop is essential:

- **Context matters:** AI can identify patterns, but it may not grasp all the nuances of your industry.
- **Quality assurance:** Humans ensure that every recommendation makes sense, adjusting for ethical considerations and potential blind spots.
- **Creative problem-solving:** Sometimes, the best solutions come from thinking outside the algorithm.

# **Agentic Workflow: An AI That Acts Like a Human**

What really sets GenBI apart is how it manages the workflow — especially when it comes to handling ambiguous queries and evolving through interaction. Our approach is designed to mimic how a thoughtful, human colleague would work through a problem.

Here’s how it works:

1. **Understanding Your Intent:** When you ask a question, the system first parses your language to understand exactly what you’re after. If there’s any ambiguity — perhaps a term that could mean more than one thing — it will ask clarifying questions, much like a real human would.
2. **Planning Data Retrieval:** Once your intent is clear, GenBI taps into the semantic layer to plan how best to fetch the necessary data. It considers various strategies and discusses them with you, ensuring that the approach aligns with your expectations.
3. **Generating the Query:** The system then generates the SQL (or the appropriate query) needed to retrieve the data. It’s not just about pulling numbers — it’s about understanding relationships and context.
4. **Iterative Feedback:** If the result isn’t quite what you expected, you have the option to refine your question or add more context. The system listens, adjusts, and learns from each interaction.
5. **Continuous Improvement:** Every interaction feeds back into the system, making it smarter over time. Eventually, GenBI builds a rich, comprehensive understanding of your organization’s unique context, ensuring that future queries hit the mark right away.

This isn’t just automation — it’s a collaborative process where the AI behaves like a helpful colleague, asking questions, clarifying needs, and even learning from missteps. It’s about making the technology work with you, not for you.

# **Conversational Interface: Data in Plain Language**

One of the biggest hurdles in traditional BI is the steep learning curve associated with technical interfaces. GenBI transforms that experience by allowing you to interact with your data in everyday language. Instead of navigating complicated menus or coding SQL, you simply ask a question as you would in a conversation.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67adf6d8bb601eb19c1c8cbf_0*6re6-gW_zlmWC2QM.png)

Wren AI — Conversational Interface

Imagine asking, “Which region performed best this quarter?” and getting an immediate, easy-to-understand response. Need to dive deeper? Follow up with, “Can you break that down by product line?” The system adapts instantly, turning complex data into digestible insights.

This conversational style not only makes BI more accessible to everyone in your organization but also encourages more frequent and meaningful data exploration.

# **Bringing It All Together: The GenBI Advantage**

In moving from traditional BI to GenBI, we’re not just adopting new technology — we’re embracing a new philosophy. Here’s what makes our approach at Wren AI truly transformative:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67696f5eef248cb314c9b533_0*QfDgSftmyc0vw20c.png)

- **Dynamic Semantic Layer:** A living map of your data that grows and refines with every interaction.
- **Human-Centered Oversight:** A blend of AI efficiency and human wisdom ensures that insights are both timely and accurate.
- **Smart, Human-Like Workflow:** An agentic workflow that understands intent, clarifies ambiguities, and learns continuously.
- **Natural Conversational Interface:** Making advanced analytics accessible to everyone, regardless of technical skill.

By integrating these elements, GenBI is not just about delivering data — it’s about driving smart decisions that propel your business forward. At Wren AI, we’re passionate about building a platform that empowers every team member, turning data into a collaborative conversation and a catalyst for growth.

# **Conclusion: Embrace the Future with Wren AI**

At Wren AI, we’re not just imagining the future of business intelligence — we’re building it, day in and day out. While our complete implementation is still a work in progress, our dedicated team is tirelessly pursuing our vision of a dynamic, intelligent, and interactive BI platform that transforms how you harness your data.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b0256228d8dabf550f_0*RXlqi_VzXyHykzpQ.png)

Wren AI — the Future of Business Intelligence

We invite you to be a part of this journey:

- **Explore Our Open Source Repository:** Check out our work on GitHub at [https://github.com/Canner/WrenAI](https://github.com/Canner/WrenAI). If you like what you see, please consider starring our repo and contributing with your ideas and expertise.
- **Discover Our Cloud Solution:** For those looking for an out-of-the-box, cloud-based solution, learn more at [https://getwren.ai/](https://getwren.ai/).

Join us as we push the boundaries of business intelligence with GenBI. Your feedback, support, and contributions are vital as we continue to innovate and shape the future of data-driven decision-making. Together, we can create a smarter, more agile, and truly transformative BI experience.

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From Traditional BI to GenBI Embracing a Smarter, More Human Approach

As we delve deeper into the vision and design of [Wren AI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi), we find ourselves on the brink of defining what we believe is the next frontier in Business Intelligence, a term we refer to as “Generative BI” or “**GenBI**.” **GenBI** signifies a paradigm shift that reimagines not only how we interact with BI tools but also how these systems are fundamentally structured. It transcends the mere evolution of interfaces; it is a complete reinvention of the core of BI architecture. At the heart of this transformation are four foundational layers that empower GenBI to redefine the future of data-driven decision-making. This is where the future of BI begins, and we’re excited to lead the charge.

Business intelligence (BI) has long been a cornerstone of data-driven decision-making, empowering organizations to leverage data for strategic insights. However, traditional BI systems have struggled to keep pace with the needs of modern enterprises. Complex interfaces, steep learning curves, and reliance on technical expertise often limit accessibility and adoption.

Enter [**Generative Business Intelligence (GenBI)**](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi) — a revolutionary approach that redefines how businesses interact with data. Powered by innovations like Wren AI, GenBI is transforming BI from a technical challenge into an intuitive, conversational experience.

# The Challenges of Traditional BI

While traditional BI tools have enabled organizations to harness data, they come with significant limitations that hinder their full potential:

### 1\. Steep Learning Curve for Non-Technical Users

BI platforms often require technical skills to navigate complex dashboards or write SQL queries. For non-technical users, this creates a dependency on IT teams or analysts, delaying access to insights and increasing frustration.

### 2\. Centralized Data Access

The reliance on technical expertise means data access is often bottlenecked. Business teams must submit requests for specific queries, slowing decision-making and reducing agility.

### 3\. Static Insights

Traditional BI systems are designed around static dashboards and pre-configured reports. They fail to offer the dynamic, on-the-fly insights needed to respond to real-time business challenges.

### 4\. Siloed and Fragmented Data

Integrating data from multiple systems remains a persistent challenge. Many BI tools struggle to provide a unified view, forcing businesses to operate with incomplete or disjointed data.

### 5\. Delayed Decision-Making

Without real-time insights, traditional BI systems often present outdated information, leading to missed opportunities and slower responses to emerging trends.

### 6\. High Cost of Ownership

The combination of training, maintenance, and IT involvement significantly increases the total cost of ownership for traditional BI tools, making them less scalable for organizations.

# Introducing Generative BI (GenBI): The Future of BI

[**Generative Business Intelligence (GenBI)**](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi) represents a paradigm shift in how organizations interact with their data. Unlike traditional BI, which relies heavily on predefined dashboards, complex interfaces, and technical expertise, GenBI uses AI and LLMs to deliver a fundamentally different experience.

By enabling users to ask questions in plain language and receive instant, context-aware answers, [GenBI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi) democratizes data access and redefines how insights are derived. It transforms BI into a dynamic, proactive system that adapts to user needs, integrating real-time data and predictive analytics to deliver insights tailored to business demands. GenBI’s ability to bridge fragmented data silos and offer unified, intuitive interactions makes it a game-changer for enterprises looking to stay competitive in an increasingly data-driven world.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/679065aadf1d8259c590a539_1*xO53E_n1ICB1JBwgBNP6TA.png)

Four Foundational Layers of [GenBI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi)

At its core, [**GenBI**](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi) **is built on four foundational layers** that enable its transformative capabilities:

### 1\. Representation Layer

This is the user-facing interface where insights are delivered in an easily digestible format. Whether through natural language responses, dynamic visualizations, or real-time dashboards, the representation layer ensures that insights are accessible and actionable for all users, regardless of their technical expertise.

### 2\. Agentic Layer

The agentic layer powers the conversational capabilities of GenBI. It leverages AI agents that can understand user queries, interpret intent, and provide contextually relevant responses. This layer enables interactive, human-like conversations with data, allowing users to ask follow-up questions and refine insights without needing to reframe their queries.

### 3\. Semantic Layer

The semantic layer acts as the intelligence behind the scenes, mapping the relationships between data points. It provides a unified framework that understands the meaning and context of data across different sources. This layer ensures that users get accurate, context-aware insights without needing to navigate the complexities of raw data structures.

### 4\. Data Layer

The data layer integrates and unifies information from various sources, such as SaaS tools, databases, and cloud storage. It ensures that data is clean, accessible, and ready for analysis. By breaking down silos and creating a single source of truth, the data layer lays the foundation for reliable and comprehensive insights.

‍

‍

At the forefront of the GenBI revolution is [**Wren AI**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=genbi), a platform designed to eliminate the barriers of traditional BI. Wren AI’s mission is to unify enterprise data and deliver insights through intuitive, conversational interfaces. Here’s how Wren AI is redefining BI:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/679065aa1c3c3754a7f63c51_1*Sr81I1fed5i86uQXXNrg-Q.png)

### 1\. Text-to-SQL Self-Service Analytics

With Wren AI, users can ask questions in natural language, and the system automatically translates these queries into SQL. This enables business users to extract insights without writing a single line of code.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/679065aa79041c4ba08acca5_1*ngVhvpyBvtlVYXJ4xYig8A.png)

### 2\. [Composable Data Framework](https://getwren.ai/post/the-new-wave-of-composable-data-systems-and-the-interface-to-llm-agents?utm_source=medium&utm_medium=post&utm_campaign=genbi) with [Semantic Engine](https://getwren.ai/post/how-we-design-our-semantic-engine-for-llms-the-backbone-of-the-semantic-layer-for-llm-architecture?utm_source=medium&utm_medium=post&utm_campaign=genbi)

At the core of Wren AI lies a powerful **semantic engine** known as the Wren Engine. This engine is designed to organize and interpret data from multiple sources — like HubSpot, Stripe, and Google Ads — using a composable data framework. Wren AI also leverages a **Modeling Definition Language (MDL)** to define and maintain semantics across all integrated systems. This ensures a unified and holistic view of enterprise operations, allowing all teams to operate with context-rich insights.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/679065aa591eaebab1efb02f_1*5vWjEWdifU8E7D7F06toIQ.png)

### 3\. AI-Driven Decision-Making

By combining generative AI with the Wren Engine’s contextual understanding and real-time data processing, Wren AI delivers actionable insights faster. The **Agentic Layer** of Wren AI employs advanced techniques such as **Chain of Thought (CoT) reasoning** and **ReAct (Reasoning and Acting) prompting**, which allow for step-by-step, context-aware workflows. These techniques enable Wren AI to intelligently analyze complex queries, break them into manageable steps, and interact with the data dynamically. When paired with the **semantic enhancements** from our Modeling Definition Language (MDL), Wren AI generates highly precise SQL queries tailored to the user’s intent. This approach not only simplifies data interactions but also ensures deep, actionable insights for even the most complex business questions

# Using Wren AI to Unlock GenBI

Wren AI simplifies the transition to Generative Business Intelligence with an intuitive process that transforms how businesses interact with data:

### 1\. Connect to Your Data Sources or SaaS Tools

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6777580c1565d24b9c5023b2_0*IxMpy-YIa7O_XFQM.png)

Easily integrate Wren AI with your existing data sources or SaaS tools like HubSpot, Stripe, and Google Ads. In just a few steps, Wren AI provides access to a unified view of your enterprise data.

### 2\. Add Semantics on Top of Your Data Sources

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/679065aab36cb81a5dea367e_0*GRTImeXfStgJi0yR.png)

Wren AI adds a semantic layer to your data, organizing it in a way that’s intuitive and accessible. In the past, semantics were stored in the minds of key personnel, limiting data usability to a select few. Now, your entire team can navigate and understand data effortlessly.

### 3\. Ask Questions and Get Answers Right Away

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67696f5effcfd0b6b8b12400_0*Jbpm2EuAyP075f34.png)

Forget the need to model metrics in traditional BI systems. With Wren AI, you simply ask business questions in natural language and receive instant answers. Dive deeper into insights by asking follow-up questions, eliminating the need to manually set up charts or model dimensions.

### 4\. One-Click to Generate Charts and Pin to Dashboards

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/679065aa18af9840e2a6cd28_1*FLFpxpqkNKrH74ho5xlZ4A.png)

AI-driven chart generation takes the guesswork out of data visualization. Wren AI selects the best-fit charts automatically, and with one click, you can pin them to dashboards. No dragging, dropping, or complex setups are required — just insights at your fingertips.

# The Future of BI: Powered by GenBI

As businesses face increasingly complex challenges, the ability to intuitively access and analyze data in real-time has become essential. Generative Business Intelligence (GenBI), with its innovative layers — from the semantic-powered Wren Engine to advanced agentic workflows like Chain of Thought (CoT) reasoning and ReAct prompting — is redefining the future of data intelligence.

**Wren AI** leads this revolution by making data not only accessible but also actionable for everyone. By integrating composable data design, semantic enhancements with our Modeling Definition Language (MDL), and intelligent workflows, Wren AI is shaping the next generation of decision-making, empowering organizations to unlock the full potential of their data ecosystem.

# Ready to Experience GenBI?

Explore how Wren AI can transform your data access strategy by [visiting our website](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=genbi). Discover how we redefine BI with our composable data framework, semantic-driven Wren Engine, and intelligent workflows.

For developers and data teams, Wren AI also offers an open-source version on [GitHub](https://github.com/Canner/WrenAI). Dive into the code and experience how a semantic-enhanced, grounded approach to text-to-SQL can revolutionize your organization’s data operations.

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AI-Powered Business Intelligence: The Complete Guide to GenBI

To understand [GenBI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=instant-reports)'s significance, it’s helpful to reflect on where we’ve come from. Traditional BI tools revolutionized how organizations approached data by offering structured ways to visualize trends, generate reports, and create dashboards. They provided clarity in the chaos of raw numbers. However, these tools often had limitations: they were static, required technical expertise, and lacked the agility to adapt to rapidly changing business questions.

Consider a marketing manager needing to analyze campaign performance. With traditional BI, they would rely on data analysts to write queries, generate reports, and build dashboards — a process that could take days or even weeks. By the time insights were delivered, the window for action might have already closed.

[**GenBI**](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=instant-reports) flips this model upside down. It introduces a dynamic, AI-driven approach in which insights are generated on the fly, dashboards are created with a click, and data analysis becomes an interactive, real-time experience. This shift from static to generative isn’t just about speed; it’s about empowering decision-makers at every level to engage directly with their data.

# Defining GenBI: What Makes It Different?

At its core, **Generative Business Intelligence** is about leveraging AI to automate and enhance the process of data analysis. Instead of relying on pre-built dashboards or static reports, GenBI systems generate insights dynamically based on the questions users ask in natural language.

Traditional BI systems often fall short because they come with steep learning curves, require extensive manual intervention, and generate static reports that don’t adapt to changing business needs. A non-technical user looking for insights into last quarter’s top-performing products might struggle navigating complex dashboards or waiting days for analysts to provide SQL-driven reports.

With GenBI, that same user can simply ask, *“What were our top-performing products last quarter, and what factors contributed to their success?”* and receive an AI-generated dashboard on the fly. Not only does GenBI produce real-time charts and contextual analysis, but it also uncovers hidden patterns and relationships that traditional BI tools might overlook. The system eliminates the need for SQL queries, accelerates decision-making, and integrates multiple data sources into a unified view — empowering teams to act on insights immediately.

# How Does GenBI Work?

The magic of GenBI lies in the seamless integration of several advanced technologies:

1. **Natural Language Processing (NLP):** This allows users to interact with data using everyday language. No need for complex SQL queries or technical jargon.
2. **AI-Driven Query Generation:** The system translates natural language questions into optimized queries that fetch relevant data from connected sources.
3. **Automated Visualization:** Instead of static reports, GenBI generates dynamic dashboards, charts in one click, based on your questions and intentions GenBI will generate the most optimal presentation for your results.
4. **Continuous Learning:** The more you use it, the smarter it gets. GenBI learns from user interactions to refine its responses and recommendations.
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b09245afe5597f58e5_0*OMyGedTA04K82ul4.png)

Four Foundational Layers of GenBI, please check the [AI-Powered Business Intelligence: The Complete Guide to GenBI](https://getwren.ai/post/the-future-of-business-intelligence-the-generative-bi-genbi?utm_source=medium&utm_medium=post&utm_campaign=instant-reports) to learn more.

This isn’t just automation; it’s intelligence in action. GenBI doesn’t replace human insight; it amplifies it, freeing up time and mental bandwidth to focus on strategic decisions.

# Get Started With GenBI. What Is Wren AI?

[Wren AI](https://getwren.ai/genbi?utm_source=medium&utm_medium=post&utm_campaign=instant-reports) is an advanced Generative BI platform designed to simplify data analysis for everyone — no technical expertise required. It allows users to access, analyze, and visualize their data instantly by simply asking questions in plain language. Whether you’re a marketer tracking campaign performance, a product manager analyzing user behavior, or a finance executive monitoring revenue trends, Wren AI transforms the way you interact with your business data.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b0256228d8dabf550f_0*RXlqi_VzXyHykzpQ.png)

Introducing [Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=instant-reports)

# Build Your First Dashboard in 3 Clicks

Getting started with Wren AI is incredibly simple. You can create a complete dashboard in just three easy steps:

## Step 1: Ask a Question in Plain Language

After connect your data in [Wren AI Cloud](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=instant-reports). Forget SQL queries and complex filters. With Wren AI, you can simply type your question in natural language. For example:

- *“What were our top-selling products last quarter?”*
- *“Show me a breakdown of website traffic by source over the past month.”*
- *“Compare revenue growth across different regions.”*
![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b03b0885f49326d6e3_1*mLrHLRPLDdP1V8-3ABBXtg.png)

Type in your question in the the input box, and click “Ask”

The AI instantly understands your intent and translates it into a structured query, retrieving the relevant data from your connected sources.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b0c1e049990f81a760_1*t6iSVI9FIYriCUMzDXLQDg.png)

Get the data right away.

You can either just check the answer, or review the SQL which how Wren AI have generated the SQL based on your question.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546af9245afe5597f58cd_1*4JaAZRG2H3L1MXYueSlqrw.png)

Step-by-step review of how the data has been generated.

## Step 2: Click ‘Generate Chart’

Once you’ve asked your question, Wren AI automatically generates the most relevant visualization for your data. Whether it’s a bar chart, line graph, pie chart, group bar chart, line chart, multi-line chart, etc, the AI intelligently selects the best format to present your insights in the clearest way possible.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b048495deeece6cadf_1*D00k8yn9-L2IwYmpHBKSBQ.png)

Click the “Chart” button to get the visualized result

You can also edit the chart for your preference

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b0d60cdefdc380f44b_1*hExkcnqFQRhhjwSv1bvMlg.png)

Click the “Edit” Icon to modify for your personal need.

# Step 3: Pin to Dashboard

With a single click, you can pin your newly generated charts to a dashboard. This allows you to track key metrics in real time, revisit insights, and share them with your team — without needing to rebuild reports from scratch.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b0e7815482e4f7807a_1*eUyT7IOv2xR7upBCTx1emQ.png)

Click the “Pin” Icon to pin to your dashbard

You can also “pin” multiple charts into one dashboard, drag-and-drop and resize it based on your preference.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546b096d2273cf4bd8bba_1*qvkMOxjkbcf9IPDEZkJTfg.png)

Now you have a “Dashboard” that you can revisit anytime

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67a546af9245afe5597f58c8_1*3QcwW8cEtfoRJMHLhVsEUA.png)

You can place charts based on your preferences.

# Why GenBI Matters

The power of GenBI, and specifically [Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=instant-reports), lies in its ability to democratize data. No longer do you need to wait for analysts or struggle with complicated BI tools — anyone in your organization can generate meaningful insights in seconds. By streamlining data workflows, GenBI enables businesses to make faster, smarter, and more data-driven decisions.

Are you ready to experience the future of business intelligence? Try [Wren AI today](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=instant-reports) and build your first dashboard in just three clicks!

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What is GenBI? Get Your First Dashboard Done in 3 Clicks

As we welcome 2025, we reflect on the remarkable progress [Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=vision) achieved in 2024. This past year was a period of transformative growth driven by invaluable lessons, relentless innovation, and the unwavering support of our partners, customers, and contributors. We set ambitious goals to redefine how enterprises approach data intelligence, and our journey so far has only strengthened our belief in the immense potential of our mission.

# **Transforming Enterprise Data Chaos into AI-Driven Intelligence**

In today’s fast-paced enterprise technology landscape, a torrent of data flows continuously from digital touchpoints, SaaS applications, and internal operational systems. This tidal wave of data offers immense potential for actionable insights while posing significant organizational challenges. Disconnected SaaS applications, fragmented data in warehouses, and rigid business rules contribute to complexity and inefficiency.

Wren AI’s mission addresses these challenges by unifying enterprise data through a comprehensive semantic framework that transforms silos into context-rich insights, enabling text-to-SQL self-service analytics and laying the foundation for future AI-driven data processes. Below, we explore the pain points in modern enterprise analytics, the key pillars of semantic data intelligence, and how Wren AI’s composable architecture — enhanced by Large Language Models (LLMs) — can fuel an AI-first data strategy ecosystem.

# **The Tectonic Shift in Enterprise SaaS**

Over the past two decades, enterprise analytics has evolved from static spreadsheets to advanced BI dashboards and machine learning solutions. Yet, many SaaS products, such as CRM, ERP, and project management systems, have grown unwieldy and are trapped by their rigidity. They risk becoming mere CRUD platforms if they do not embrace the next wave of AI-driven semantic data agents capable of unifying disparate data, automating decisions, and orchestrating workflows.

The enterprise analytics of tomorrow must provide real-time, context-aware insights at scale. Wren AI’s vision empowers businesses to seamlessly integrate data from various systems, enhance it with semantic understanding, and unlock real-time, AI-driven outcomes.

# **The Current Challenges in Enterprise Analytics**

Despite significant investments, organizations continue to face challenges with:

1. **Fragmented Data Sources**: CRM data may reside in Salesforce, marketing analytics in Google Analytics, and operational details in SAP. With each system operating in isolation, teams lack a comprehensive enterprise-wide perspective.
2. **Rigid Business Rules**: Hardcoded logic within analytics pipelines often fails to adjust to new data or shifting market conditions.
3. **Limited Contextual Insights**: When metrics stand alone, they lose their relational context — like how a marketing interaction relates to future product usage.
4. **Siloed Analytics Teams**: Analysts often spend more time cleaning and reconciling data than generating proactive insights

# **Pillars of Semantic Data Intelligence**

Wren AI is designed around four core pillars:

1. **Contextual Data Modeling:** Rather than viewing data points as separate metrics, knowledge graphs, and ontologies illustrate the relationships between customers, products, and transactions. A semantic approach clarifies how “sales call,” “marketing touch,” and “email open” are interconnected, allowing for nuanced analysis and consistent definitions across departments.
2. **Multi-Touchpoint Attribution:** Simple first- or last-touch models seldom capture the reality of multi-channel, multi-step customer journeys. Wren AI’s semantic intelligence documents each interaction (marketing channel, sales call, or service event) in a knowledge graph, enabling Real-time attribution to comprehend each channel’s impact on conversion or upsell, Hyper-personalized offers derived from behavioral data, Adapting attribution logic as market conditions evolve.
3. **Unified Semantic Data Knowledge Management:** Enterprises often manage multiple warehouses (e.g., Snowflake, Databricks) and various SaaS applications (Salesforce, Microsoft Dynamics, HubSpot, Stripe, etc.). Wren AI streamlines navigation through these complex schemas by applying semantic metadata on top of disparate sources. Insights generated (e.g., high-likelihood churn alerts) can automatically feed back into operational systems — a process known as reverse ETL — creating closed-loop data activation.
4. **AI-Augmented Operational Insights:** AI-driven semantic agents act as the “brain” of enterprise operations, monitoring real-time data streams, recommending immediate actions, and orchestrating workflows. With a unified semantic model as a foundation, these AI agents can interpret and automate decisions that once required manual oversight. Wren AI’s architecture is built to support these capabilities through advanced LLMs and agentic pipelines.

# **Use Cases: Where Semantic Data Intelligence Shines**

## **Customer 360 and Hyper-Personalization**

Delivering a unified view of the customer is notoriously challenging. Wren AI brings data together from CRM, ERP, marketing, and support platforms, offering a 360-degree customer perspective. Businesses can then deliver targeted promotions, personalized support, and context-aware upselling across the entire lifecycle.

## **Marketing Optimization with Multi-Touch Attribution**

Modern marketing spans email, social media, search ads, webinars, and more. Without robust multi-touch attribution, it is difficult to identify which channel contributes most to conversions. Wren AI’s intelligence tracks each customer touchpoint and lets teams pose natural language queries like, “Compare average CAC between direct email campaigns and social ads over the last 12 months.” This self-service approach reduces reliance on specialized data analysts.

## **Operational Efficiency in Supply Chain and Logistics**

For global organizations, minor supply chain hiccups can have major repercussions. Wren AI models relationships among suppliers, shipping, and sales orders, predicting downstream effects of disruptions and triggering real-time mitigation strategies.

# **Introducing Wren AI: Vision, Architecture, and Text-to-SQL**

Recognizing these industry-wide challenges, Wren AI provides a composable data architecture that meshes with existing data sources and platforms. Wren AI promises practical and transformational AI-driven analytics by overlaying a semantic model across enterprise data and coupling it with text-to-SQL via LLM interfaces.

## **Wren AI’s Modeling Definition Language (MDL)**

MDL lies at the heart of Wren AI, capturing and codifying:

1. **Metadata and Schemas:** Mapping fields and tables to relevant business concepts
2. **Business Terminologies**: Aligning to departments’ specific language — e.g., “leads” or “service requests”
3. **Data Policies and Governance:** Enforcing access controls for compliance-heavy environments
4. **Aggregations and Calculations:** Centralizing rules for metrics like revenue, LTV, or profitability
5. **Semantic Relationships:** Structuring how marketing campaigns connect to leads, opportunities, and invoices

By establishing one semantic layer, organizations eliminate inconsistent definitions scattered across systems. This unified layer underpins advanced text-to-SQL and AI-driven insights.

## **LLM Integration for Advanced Text-to-SQL**

Wren AI integrates a powerful text-to-SQL interface with its LLM layer, enriched by enterprise-specific documents and domain knowledge. Even non-technical users can ask sophisticated questions — “*Show me how many Q3 leads turned into closed-won deals, grouped by industry*” — in plain language.

Under the Hood

1. **Retrieval-Augmented Generation (RAG):** The system references a domain-specific knowledge base, pulling guidelines and best practices to create more accurate, context-rich answers.
2. **Domain-Specific Models:** By fine-tuning LLMs for industries like retail or finance, the text-to-SQL tool understands sector terminology (e.g., “claims,” “SKU forecasting”).
3. **Chain of Thought (CoT) reasoning and ReAct (Reasoning and Acting) prompting techniques:** Combining with semantic enhancements from our Modeling Definition Language (MDL) to intelligently generate precise SQL queries.
4. **Optimized SQL Generation:** Wren AI executes queries against Snowflake, Databricks, MySQL, or other data stores seamlessly.
5. **Actionable Insights:** Rather than returning raw SQL, Wren AI provides human-readable results by AI — such as summaries, tables, charts, or dashboards — for faster decision-making.

This approach democratizes analytics, cutting dependence on data engineers and empowering every team member. Meanwhile, the synergy between LLMs and Wren AI’s semantic framework ensures consistent definitions across the organization.

## **Composable Data Systems and the Semantic Layer**

Wren AI adopts a composable architecture, enabling enterprises to integrate it without a complete tech overhaul:

- **Data Source Layer:** Compatible with traditional databases (MySQL, PostgreSQL) and modern warehouses (Snowflake, Databricks).
- **Execution Layer:** Interoperates with query engines like Velox, Apache DataFusion, and DuckDB.
- **Open File & Table Formats:** Supports Apache Iceberg, Delta, and Parquet for data portability and minimal vendor lock-in.

By offering a single semantic lens atop these composable layers, Wren AI reduces redundant data transformations, fosters cross-domain collaboration, and future-proofs enterprises for the era of AI-driven workflow orchestration.

## **Looking into the Future Roadmap**

As AI continues to accelerate, Wren AI is committed to:

1. **Deeper AI Orchestration:** Building on text-to-SQL to develop AI “agents” for automated data integration and predictive policymaking — aligned with the vision of AI-first enterprises.
2. **Industry-Specific Ontologies:** Offering out-of-the-box semantic models for verticals like healthcare, finance, and manufacturing, speeding adoption and time to value.
3. **Augmented Decision Intelligence:** Going beyond insights to enable real-time triggers — e.g., automatically reassigning supply chain resources when a risk is detected.
4. **Collaborative Ecosystem:** Working with partners, integrators, and customers to co-create specialized semantic models, spurring continuous innovation.

# **Conclusion**

The enterprise technology landscape is at a critical juncture. The transition to an AI-first world necessitates new foundations that are open, composable, and deeply semantic.

[Wren AI](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=vision) is created to serve as that foundation. By integrating contextual data modeling, multi-touchpoint attribution, unified data management, and AI augmentation — backed by advanced LLM interfaces — business and technical leaders can thrive in an era where intelligent agents handle not just isolated tasks but entire streams of information and decision-making.

In this new paradigm, the capability to integrate fragmented data and provide real-time, context-aware insights will define competitive organizations. Wren AI is poised to empower these organizations, fostering a future where AI facilitates meaningful, end-to-end business transformation.

#### **Acknowledgment**

We extend our heartfelt gratitude to [**Cheng Wu**](https://www.linkedin.com/in/cheng-wu-4614a8a/) for his invaluable insights and feedback that significantly enhanced this article. His expertise and thoughtful contributions have been instrumental in shaping the vision and clarity of this post. Thank you, Cheng, for your support and for being an integral part of this journey forward to 2025.

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Forward to 2025 Powering the Future of Enterprise with AI-Driven Data Intelligence

In today’s data-driven marketing and sales landscape, businesses using HubSpot need more than streamlined workflows — they need actionable insights derived from unified data. [HubSpot’s Breeze AI](https://www.hubspot.com/products/artificial-intelligence) effectively automates repetitive tasks and improves operational efficiency, but it stops short of delivering cross-platform analytics essential for strategic decision-making. Enter [**Wren AI Cloud**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=hs_breeze_ai), the ultimate data integration and analytics solution designed to amplify your HubSpot investment by merging internal CRM data with external sources for comprehensive, data-driven insights.

In this article, we’ll explore how Wren AI Cloud complements HubSpot and [Breeze AI](https://www.hubspot.com/products/artificial-intelligence), providing a holistic data strategy that drives smarter decisions and scalable growth.

# The Data Challenge: Growing Complexity in a Multi-Platform World

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6777580c1565d24b9c5023b2_0*IxMpy-YIa7O_XFQM.png)

Connecting dots with from HubSpot internal and external sources

As businesses expand, so do the tools and platforms they use to manage operations. Sales teams depend on CRM data from HubSpot, marketing teams use Airtable for lead management, product teams monitor inventory through WooCommerce, and finance teams rely on Google Sheets or data warehouses like Snowflake. This fragmented data landscape makes it difficult to consolidate insights and act on opportunities.

**The problem?** Data silos limit visibility. Without integrated analytics, your sales forecasts may overlook market trends, and your marketing strategies might fail to account for inventory fluctuations. Operational efficiency alone isn’t enough — you need a unified, actionable data strategy.

# [HubSpot Breeze AI](https://www.hubspot.com/products/artificial-intelligence) vs. [Wren AI Cloud](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=hs_breeze_ai): Complementary Tools for Growth

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/676427fcf9be13480e134c13_0*89iFBho21kXS5QnQ.png)

**HubSpot Breeze AI** focuses on automating internal processes. It excels at handling repetitive tasks, predicting outcomes, and guiding actions within the HubSpot CRM. Breeze AI streamlines workflows, freeing teams to focus on what matters most.

[**Wren AI Cloud**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=hs_breeze_ai), however, takes your data strategy further. It bridges the gap between your CRM and external data ecosystems, offering multi-source insights critical for informed decision-making. By integrating data from tools like Google Sheets, Airtable, and cloud databases, Wren AI Cloud provides a 360-degree view of your business performance.

**Together**, Breeze AI and Wren AI Cloud create a robust, data-driven ecosystem:

- **Breeze AI:** Manages day-to-day operations within HubSpot.
- **Wren AI Cloud:** Delivers comprehensive analytics by merging internal and external data.

# Key Features of Wren AI Cloud for HubSpot Users

## 1\. Unified Data Insights Across Platforms

Wren AI Cloud seamlessly integrates HubSpot with external data sources, eliminating silos. Whether it’s sales data in HubSpot, marketing performance in Airtable, or financial data in Google Sheets, Wren AI Cloud brings it all together for a unified view.

## 2\. Text-to-SQL Simplicity

No more complex SQL queries. Wren AI Cloud democratizes data access with its **text-to-SQL** feature. Simply type questions like, *“Show me total revenue from Q3 deals alongside website traffic data,”* and get instant results. This functionality removes technical barriers and empowers all teams to leverage data.

## 3\. Automated Reporting and Dashboards

Wren AI Cloud automates the creation of weekly, monthly, or quarterly reports, saving hours of manual data compilation. Generate executive dashboards that blend CRM metrics with marketing, sales, and financial data — all in one place.

## 4\. Data Quality Auditing

Dirty data undermines your CRM’s value. Wren AI Cloud cross-references HubSpot records with external databases to identify duplicates, outdated contacts, or misaligned pipeline stages. This ensures clean, reliable data for decision-making.

## 5\. In-Depth Marketing ROI Analysis

Accurately measure marketing effectiveness by linking HubSpot campaign data with external ad spend metrics. Identify which campaigns deliver the highest ROI and optimize your budget for maximum impact.

# **Different Approaches, Complementary Value**

It’s easy to see why some might compare Wren AI Cloud to Breeze AI, but they serve distinctly different purposes:

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67892df13999f2a11acaf970_1*-oG4kZ_Qm6wqbJQuS81ZnA.png)

Wren AI Cloud vs. Breeze AI

By leveraging both tools, you build a data-driven ecosystem where Breeze AI manages the day-to-day operations, and Wren AI Cloud provides the macro-level insights. Together, they support your entire business workflow — from granular tasks to big-picture analytics.

# Real-World Use Cases of [Wren AI Cloud](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=hs_breeze_ai)

## Data Quality Auditing and Validation

Maintaining clean data in HubSpot is crucial. Wren AI Cloud automatically audits CRM records by comparing them to verified external data. This process reduces errors and ensures your sales and marketing teams work with the most accurate information.

## Executive Reporting and Dashboards

Executives need high-level insights that transcend individual platforms. Wren AI Cloud consolidates HubSpot pipeline data, marketing outreach performance, website traffic, and financial metrics into dynamic dashboards. These reports provide leadership with actionable insights for strategic decisions.

## Marketing Attribution and ROI Tracking

Marketing teams often struggle to connect campaigns with tangible results. Wren AI Cloud enables full-funnel attribution by blending HubSpot lead data with ad spend information from Google Sheets or other sources. This end-to-end analysis helps refine marketing strategies and justify investments.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/676427fc0c13c0c3702ddbc0_0*qqOp2ChLE99tkVA_.png)

# **Getting Started: Launch** [**Wren AI Cloud**](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=hs_breeze_ai) **via the HubSpot Marketplace**

[Wren AI Cloud](https://getwren.ai/?utm_source=medium&utm_medium=post&utm_campaign=hs_breeze_ai) was recently approved on the HubSpot Marketplace, making it easier than ever to get started. The integration is seamless, allowing you to quickly connect HubSpot to your external data sources and start querying data in minutes.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67892df0a3ea07e227d5b0ef_1*Z6waq4Ji0WtVq6L1YpcsBQ.png)

## **Getting Set Up**

1. **Install Wren AI Cloud from the HubSpot Marketplace:** Visit the [Wren AI Cloud listing](https://app.hubspot.com/ecosystem/19644562/marketplace/apps/wren-ai-cloud) to install the app.
2. **Connect Your Data Sources:** Link your HubSpot account and any external databases, spreadsheets, SaaS platforms, or data warehouses you want to analyze.
3. **Begin Exploring Data with Plain Text:** Start asking questions in plain English — Wren AI Cloud will handle the complexity. For instance, “Compare our HubSpot closed-won deals revenue to e-commerce sales over the last quarter.”
4. **Build Dashboards and Set up Reports:** Create weekly, monthly, or quarterly dashboards to track performance, monitor data quality, and inform strategic decisions.

# Why Wren AI Cloud is the Ideal Solution for HubSpot Users

In an era where data-driven decisions define business success, HubSpot users need more than automated workflows. They need a strategic data partner. Wren AI Cloud fills that role by:

- **Breaking data silos** and providing holistic insights.
- **Simplifying data access** with natural language queries.
- **Automating complex reporting** to save time and drive action.
- **Empowering teams** to make data-informed decisions without technical barriers.

By integrating seamlessly with HubSpot, Wren AI Cloud turns fragmented data into a powerful asset, enabling businesses to scale smarter and faster.

# Conclusion: Elevate Your Data Strategy with Wren AI Cloud

Operational efficiency is just one piece of the puzzle. To truly unlock growth, businesses need deep, multi-source insights. HubSpot Breeze AI optimizes day-to-day tasks, but Wren AI Cloud delivers the strategic intelligence required for long-term success.

Wren AI Cloud empowers your entire organization to harness data effectively, from auditing data quality and generating executive reports to conducting in-depth ROI analysis.

**Ready to transform your HubSpot data strategy?**

🚀 [**Install Wren AI Cloud on the HubSpot Marketplace Now**](https://app.hubspot.com/ecosystem/19644562/marketplace/apps/wren-ai-cloud) **🙌**

Unlock unified insights, empower your teams, and drive smarter decisions with Wren AI Cloud — the key to a fully integrated, data-driven future.

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Unlock the Full Power of HubSpot with Wren AI Cloud

Text-to-SQL systems promise a future where anyone — technical or not — can interact with data through natural language. Picture a marketing manager asking, *“What was our top-selling product line last quarter?”* and getting an immediate, accurate response without writing SQL. The potential to democratize data access is immense. It can liberate teams from data silos, speed up decision-making, and foster a more data-driven culture across organizations.

However, achieving this ideal state isn’t trivial. One of the most persistent challenges is the phenomenon of “**hallucinations**.” Within the world of large language models (LLMs) and text-to-SQL solutions, hallucinations occur when the system returns queries that reference nonexistent tables or columns, invents metrics, or produces logically incorrect constraints. The model might be confident, and the SQL might even look syntactically correct, but the query doesn’t align with your actual database schema or semantic definitions. The result is wasted time, eroded trust, and potentially misguided decisions.

In this article, we’ll explore why hallucinations occur in text-to-SQL, their real-world consequences, and the techniques that can help reduce them. We’ll also highlight how Wren AI tackles these issues through a holistic approach that includes schema awareness, iterative validation, semantic modeling with its Metadata Definition Language (MDL), and continuous feedback loops. While we’ll discuss Wren AI’s contributions, the principles here apply widely to any organization looking to improve reliability and trust in text-to-SQL solutions.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/677c96054b484a7cf98af3c7_0*sbP3I8Al3WAplc5B.png)

# **Defining Hallucinations in Text-to-SQL**

A “hallucination” is when a model produces output not grounded in the provided context or facts. For text-to-SQL tasks, this means the system generates SQL queries that reference columns, tables, or filters that don’t exist. For instance, consider a database with a single sales table containing product\_id, units\_sold, and sale\_date. A hallucinated query might look like:

SELECT product\_name, revenueFROM salesWHERE region = 'EMEA';

`product_name`, `revenue`, and `region` aren’t in the schema. The model fabricated them because it associated these terms with typical sales-related queries without checking against the actual database structure.

# **Why Do Hallucinations Occur?**

Hallucinations primarily arise because large language models (LLMs) generate text based on patterns, not schema awareness. Key factors include:

- **Lack of Schema Grounding:** Without explicit, up-to-date schema information, the model guesses columns or tables.
- **Over-Generalization from Training:** Models may rely on patterns seen in training data that don’t fit your database.
- **Underspecified Queries:** Vague or ambiguous user questions invite the model to “fill in the gaps.”
- **Unconstrained Creativity:** If not reined in, the model’s generative capabilities can produce plausible-sounding but invalid queries.

# **The Real-World Impact of Hallucinations**

When hallucinations occur regularly, the consequences can be severe:

- **Eroded Trust:** Users quickly lose confidence if the system consistently produces nonsense.
- **Wasted Resources:** Technical staff must diagnose bad queries, negating the productivity gains text-to-SQL promises.
- **Misguided Decisions:** A query that slips through validation might lead to incorrect insights and poor strategic choices.

# **Foundations for Reducing Hallucinations**

To minimize hallucinations, focus on “grounding” the model’s output in actual schema and semantic rules:

- **Schema Provisioning:** Always provide the model with your database schema.
- **Validation Steps:** Integrate layers of checks before showing a query to the user.
- **Iterative Reasoning:** Encourage the model to break down its reasoning to catch inconsistencies early.

# **Techniques to Minimize Hallucinations**

A combination of strategies can significantly reduce hallucinations.

## **Schema Clarity and Explicit Definitions**

- **Detailed Schema Documentation:** Enumerate all tables, columns, data types, and relationships.
- **Contextual Metadata:** For ambiguous column names, provide context. For example, clarify cust\_id as “customer identifier,” helping the model map queries correctly.

## **Retrieval-Augmented Generation (RAG)**

- **On-Demand Schema Provision:** Dynamically fetch schema details at query time. The model works with fresh, accurate information, leaving less room for guesswork.
- **Query Blueprints:** Keep a repository of template queries. Rather than inventing new patterns, the model adapts proven ones.

## **ReAct (Reasoning + Acting) and Chain of Thought (CoT) techniques**

- **Improved Query Accuracy**: CoT guides the model to break down complex SQL queries step by step, reducing errors in query generation.
- **Dynamic Error Handling:** ReAct allows the model to reason through query errors and iteratively refine SQL commands, ensuring more accurate outputs.
- **Complex Query Generation:** By combining reasoning (CoT) with real-time actions (ReAct), the model can handle multi-step queries and nested SQL logic more effectively.
- **Adaptive to Schema Variations:** ReAct can interact dynamically with database schemas, adapting the query to schema changes or ambiguities.

## **Fine-Tuning on Domain-Specific Data**

- **Domain-Specific Training:** Show the model examples of correct queries against your schema. Fine-tuning aligns the model’s understanding with real-world data structures.
- **Incremental Updates:** Continually refresh training as your schema changes, ensuring the model stays current.

## **Guardrails, Query Validation, and Dry-Runs**

- **Schema Validation:** Automatically check if all columns and tables referenced by the generated SQL exist.
- **Semantic and Syntax Checks:** Use SQL parsers to confirm the query is syntactically valid.
- **Dry-Run Execution:** Before showing results to the user, attempt running the query against a test instance or in a safe mode. If the database returns errors (e.g., unknown column), prompt the system to correct itself. Dry-running catches hallucinations at the source, as it forces the model to align with actual database feedback.

## **Encouraging Transparency and Explainability**

- **Chain-of-Thought Prompting:** Ask the model to articulate its reasoning steps internally.
- **User Review:** Optionally display a reasoning summary for advanced users, allowing them to verify the query logic.

# **Introducing Wren AI**

Before we dive deeper into how Wren AI tackles hallucinations, let’s briefly introduce what Wren AI is and why it matters.

Wren AI is a text-to-SQL solution designed to bridge the gap between non-technical users and complex data ecosystems. Its goal is to enable anyone — analysts, product managers, marketing leads, executives — to ask questions in plain language and get accurate, meaningful answers from their databases. Wren AI stands out by blending powerful large language model (LLM) capabilities with structural safeguards, semantic modeling, and iterative feedback loops. This combination transforms raw SQL querying into a more natural, intuitive experience without sacrificing correctness.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/677c9605d88c03340dfd1454_0*2XRnDMQCpyCa0TaX.png)

Where many text-to-SQL systems struggle with hallucinations and brittle logic, Wren AI adopts a holistic approach. From schema-first context provision to semantic layers defined by MDL, from transparent reasoning steps to dry-run validation, Wren AI ensures that the answers you get are grounded in your actual data and business definitions.

# **The Role of the Semantic Layer and MDL**

While schemas define the physical structure of your data, organizations often need a higher-level semantic layer that encodes business meaning. This semantic layer ensures that everyone — from data engineers to business analysts — interacts with data consistently. Instead of every user interpreting raw fields in their own way, the semantic layer provides a canonical model of the data domain. It defines how entities, attributes, metrics, and dimensions relate to each other, all anchored to the underlying physical schema.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/66c41befc04f13f217ce1d5e_1*xunAEpVzfkvmHMBLvuIqxw.png)

**Key Aspects of MDL:**

## **Define Entities and Attributes:**

Instead of scattering relationships across multiple queries or dashboards, MDL lets you define business concepts as entities (like “Customers” or “Products”) and their properties as attributes. These can be mapped to one or more physical database tables.

## **Express Metrics and Dimensions:**

MDL centralizes the definition of metrics (like “Total Revenue” or “Average Order Value”) and their related dimensions (time periods, regions, categories). This ensures canonical definitions and consistency across all queries.

## **Encapsulate Logic:**

Transformations, filters, and derived metrics live in one place. Instead of rewriting logic in each SQL query, you record it once in MDL. This reduces the risk of hallucinations because the model refers to a single source of truth. Rather than guessing relationships or metrics, the model uses the semantic layer defined by MDL to produce grounded queries.

By leveraging MDL, Wren AI doesn’t just rely on raw schema inputs; it taps into a semantic model that aligns the LLM’s outputs with your business reality. Entities, attributes, metrics, and relationships are defined once, ensuring that even complex queries have a stable reference point. This makes hallucinations less likely because the model can’t arbitrarily invent entities or metrics that don’t exist in the MDL-based semantic layer.

# **How Wren AI Approaches the Hallucination Problem**

Wren AI employs a multifaceted strategy to reduce hallucinations:

## **Schema-First Philosophy:**

Wren AI ensures the model always operates with explicit schema context. The schema is provided as part of the prompt, setting clear boundaries on what the model can reference.

## **Contextual Query Routing:**

If a user request doesn’t align with the known schema or semantic layer, Wren AI can gracefully refuse or redirect the query. This prevents the model from attempting to invent columns or tables it can’t find.

## **Step-by-Step Reasoning and Transparency:**

Wren AI encourages the model to break down its reasoning before finalizing a query. This chain-of-thought approach helps catch errors early. The system can surface this reasoning to users who want to understand how the query was formed.

## **Built-In Validation and Dry-Runs:**

After generating a query, Wren AI performs a validation step. It attempts a dry-run against your database. If the query fails — due to a missing column or a syntax error — the system uses that feedback to adjust and regenerate the SQL. This immediate loop of trial and correction drastically reduces hallucinations.

## **Semantic Layer with MDL:**

With MDL, Wren AI’s semantic layer ensures that the model doesn’t just know what tables and columns exist — it also knows the business meaning behind them. By drawing on a carefully curated semantic model, the system is less likely to produce irrelevant or incorrect fields. The model looks up the defined metrics and dimensions in MDL instead of inventing them, reducing the cognitive load on the LLM and minimizing guesswork.

In essence, Wren AI’s approach involves combining robust schema grounding, semantic modeling, reasoning transparency, and iterative validation. Each element targets a different root cause of hallucinations, making the entire pipeline more reliable.

# **Try it yourself with Wren AI!**

Hallucinations in text-to-SQL are not an inevitability. With the right strategies — schema grounding, semantic modeling, retrieval augmentation, validation steps, transparency, and iterative improvement — it’s possible to significantly reduce their occurrence and impact. The result is a system that everyone, from analysts to executives, can rely on for trustworthy insights.

Wren AI embodies these principles. It leverages a schema-first approach, dry-run validations, and the powerful Metadata Definition Language (MDL) to ensure that the underlying semantic layer is not only well-defined but also fully integrated into the query generation process. By doing so, Wren AI drastically lowers the chances of hallucinations and fosters a more confident, data-driven culture.

**Want to learn more? Check our related articles 👇**

- [Why the Semantic Layer is Essential for Reliable Text-to-SQL and How Wren AI Brings it to Life](https://getwren.ai/post/why-the-semantic-layer-is-essential-for-reliable-text-to-sql-and-how-wren-ai-brings-it-to-life?utm_source=medium&utm_medium=post&utm_campaign=cross)
- [The new wave of Composable Data Systems and the Interface to LLM agents](https://getwren.ai/post/the-new-wave-of-composable-data-systems-and-the-interface-to-llm-agents?utm_source=medium&utm_medium=post&utm_campaign=cross)
- [How we design our semantic engine for LLMs? The backbone of the semantic layer for LLM architecture.](https://getwren.ai/post/how-we-design-our-semantic-engine-for-llms-the-backbone-of-the-semantic-layer-for-llm-architecture?utm_source=medium&utm_medium=post&utm_campaign=cross)

**Ready to take the next step?**

Check out our website at [https://getwren.ai/](https://getwren.ai/) to learn how Wren AI can transform your data access strategy.

We also offer an open-source version on GitHub: [https://github.com/Canner/WrenAI](https://github.com/Canner/WrenAI). Explore the code, try it out, and see firsthand how a semantic-driven, grounded approach to text-to-SQL can make a real difference in your organization.

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Reducing Hallucinations in Text-to-SQL Building Trust and Accuracy in Data Access

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How Uber is Saving 140,000 Hours Each Month Using Text-to-SQL — And How You Can Harness the Same Power

As the last day of 2024 unfolds, I can’t help but feel immense gratitude and pride reflecting on Wren AI’s journey this year. The momentum we’ve built — both as an open-source project and as a commercial service — has been nothing short of remarkable, and I’d like to share some highlights and extend our heartfelt thanks to everyone who’s been part of the story.

# **Open Sourcing Wren AI & Community Growth**

Back in April, we took a significant leap by open-sourcing Wren AI. Since then, the community has rallied around the project in ways we never anticipated. Here are a few achievements we’re celebrating:

## **2,670+ GitHub Stars**

We surpassed a major milestone on [GitHub](https://github.com/Canner/WrenAI), standing proud among top Text-to-SQL libraries.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6773adea9cf3d398120c7db1_1*mQMlH271qc_r-MKBdjq6wg.png)

## **30+ Contributors**

We welcomed more than 30 developers, data enthusiasts, and AI experts who have contributed code, documentation, and insightful feedback.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6773adea68998bd46a74d3f1_1*KB-bWtlCcZYcJnlexMjvQA.png)

## **Expanded Data Source Support**

Over the course of this year, we added compatibility with a wide range of data sources, including BigQuery, DuckDB, PostgreSQL, JSON, Parquet, CSV, MySQL, MSSQL, Trino, and ClickHouse, making Wren AI more accessible and versatile than ever.

The open-source community has helped Wren AI become one of the most widely used Text-to-SQL solutions in the field. Your collaborative spirit, bug reports, feature requests, and pull requests have been invaluable in shaping our roadmap.

# **Launching Wren AI Cloud**

In October, we introduced our commercial offering, [**Wren AI Cloud**](https://getwren.ai/), making it easier for enterprises and analytics teams to harness the power of Text-to-SQL without needing to manage their own infrastructure. Here’s a look at some of the features we rolled out:

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## **Boilerplates for Streamlined Analysis from SaaS Tools**

A standout addition this year has been **Boilerplates**, which are pre-defined templates that streamline your data analysis process. Boilerplates have proven incredibly useful for anyone looking to get up and running with Wren AI quickly — especially when working with popular SaaS tools like **HubSpot** and **Google Analytics 4**. We’ll soon expand support to include **WooCommerce**, **Google Ads**, **Facebook Ads**, **Instagram Ads**, **Stripe**, and more so your data workflows can be even more seamless.

## **AI-Powered Insights & Visuals**

We listened to your requests for more intuitive data interpretation. In response, we introduced AI-driven charting and visualization features — helping you transform raw data into clear, actionable charts with just a few clicks or simple prompts.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/67696f5e169db500739ed0a7_0*AGHwuG-UFcn5f3qA.png)

## **Project & User Management**

Managing multiple data projects across various teams can be challenging. That’s why we introduced **Project Management and User Management** features in Wren AI Cloud, giving you better control and visibility over who is working on what and how they’re interacting with sensitive data.

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/676427fc0c13c0c3702ddbc0_0*qqOp2ChLE99tkVA_.png)

## **Advanced Data Controls for Teams and Companies**

Security and compliance go hand in hand with accessibility. We rolled out **Advanced Data Controls** to help organizations govern data more effectively. Role-based permissions, audit logs, and dataset-level access controls are just a few ways we’re enabling teams and companies to stay both efficient and compliant.

# **Looking Ahead to 2025**

We’re entering the new year with ambitious goals. Here’s a sneak peek at what’s on our roadmap:

## **Automation**

We plan to integrate **automation tools** to streamline data workflows and analysis. This will allow users to automate repetitive tasks, connect Wren AI with hundreds of other apps, and build custom workflows without writing code.

## **Advanced Data Analytics via AI**

Our team will continue to refine our AI models to handle more complex queries, deeper insights, and richer data relationships — turning raw data into meaningful conclusions.

## **Autonomous Actions**

Looking beyond insights, we want Wren AI to *act* on those insights, enabling automated decision-making and self-improving pipelines based on the information the AI uncovers.

## **Dashboards & Reporting**

One of our most requested features for 2025 is **Custom Dashboards and Reporting**. We’re building tools that will allow companies to save and organize AI-generated visuals directly into customizable dashboards for easy reference and easily share dashboards across teams or export them for presentations and deeper analysis.

This feature will empower businesses to seamlessly integrate AI insights into their reporting workflows, ensuring data-driven decisions are always at their fingertips.

# **A Heartfelt Thank You**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/6773adebb465cbb3f4f7f723_1*p7a0GbnvLZWR0TXuzChCCQ.png)

Every star on GitHub, every user sign-up, every thoughtful comment — each of these moments reaffirms our mission: to make data analysis simpler, more collaborative, and more impactful. We couldn’t have accomplished any of this without the ongoing support, creativity, and feedback from you, our community and customers.

As we bid farewell to 2024, we are tremendously excited about the next chapter of Wren AI. From all of us here, thank you for being a part of this journey. Here’s to a new year filled with continued innovation, growth, and breakthroughs in data-driven insights.

**Cheers to 2025!** We can’t wait to show you what’s next.

# **Get Involved and Experience Wren AI**

🔹 **Open Source Wren AI**: Explore our GitHub repository at [https://github.com/Canner/WrenAI](https://github.com/Canner/WrenAI) to understand the codebase, contribute, or deploy in your environment.

🔹 **Wren AI Cloud**: For a fully managed, enterprise-grade solution, head to [https://getwren.ai/](https://getwren.ai/) and discover how Wren AI Cloud can transform your data operations.

👉 **Join us in shaping the future of data analysis!** Whether you’re contributing to the open-source project or adopting Wren AI Cloud, your journey toward unlocking faster insights starts today.

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Wren AI 2024 Year in Review A Fruitful Journey and Exciting Plans Ahead

From our team at [Wren AI](https://getwren.ai/), the most common question we hear from our users is: *“Can we just connect Wren AI’s Text-to-SQL solution directly to our raw data? Isn’t data modeling unnecessary now that AI can figure it all out?”* While it’s true that modern AI-driven tools like [Wren AI](https://getwren.ai/) have revolutionized how we interact with data, the idea that data modeling no longer matters is a misconception. On the contrary, clean, well-structured, and business-oriented datasets are more essential than ever. Without this strong foundation, AI solutions struggle to translate natural language queries into reliable insights.

In this article, we’ll explain why data modeling isn’t dead in the era of Text-to-SQL, highlight the limitations of relying solely on raw data, and show how you can still borrow from traditional BI best practices to optimize your Wren AI implementation. The goal is not to eliminate modeling but to leverage it so that Wren AI can deliver the accurate, intuitive, and actionable analytics experience your business users expect.

# Preface

Data analytics has always been about turning raw information into actionable insights. Historically, this process required specialized data analysts and BI developers who wrote complex SQL queries, built semantic layers, and carefully modeled data. In recent years, text-to-SQL tools have emerged, promising a new era of direct, natural language interaction with data.

But as these technologies advance, a question often arises: *Is data modeling dead?* After all, if anyone can just ask questions in plain English and get answers, why bother with complex data modeling, semantic layers, or precomputed metrics?

The reality is that data modeling is more crucial than ever before. While Text-to-SQL solutions like [Wren AI](https://getwren.ai/) offer a powerful new interface for data consumers, they are most effective when built on top of a strong, well-structured data foundation. In this article, we’ll explore the current limitations of text-to-SQL, discuss why data modeling remains essential, and provide concrete examples from traditional BI implementations. We’ll also outline how you can leverage your existing modeling work to get the most out of [Wren AI](https://getwren.ai/) — ensuring that data democratization doesn’t come at the expense of accuracy, consistency, and reliability.

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Using Wren AI to retrieve data from databases

# **The Rise of Text-to-SQL: Why All the Buzz?**

Traditional BI often involves specialized tools and skill sets: data analysts craft SQL queries, BI developers create dashboards, and data engineers manage the ETL/ELT pipelines. This meant that business users — product managers, marketing professionals, or sales executives — had to rely on intermediaries to get the insights they needed. Text-to-SQL tools emerged as a response to this bottleneck, promising to let anyone interact with data directly.

Instead of writing a complex SQL query, a user could simply type:

*“Show me total revenue by product category for Q3, and highlight the top five categories.”*

A text-to-SQL tool interprets this request, translates it into SQL, and returns the result.

**However, there’s a catch:** The tool must know what “revenue” means, how products relate to categories, what timeframe Q3 refers to, and what “top five” implies. If your underlying data is a tangled web of cryptically named tables and columns, such as `tbl_trx_2023`, `cust_id_num`, `cat_desc_var1` — the text-to-SQL engine will struggle. It might misinterpret fields, produce incorrect logic, or fail to return any meaningful result.

This is where traditional data modeling comes into play.

# **Why Data Modeling Is Still Essential**

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## **1\. Clarifying Business Concepts**

In a traditional BI setup, data modeling transforms raw source tables into business-friendly datasets. For example, let’s say you have a transactional system that stores product sales. Your raw data might be split across multiple tables:

- orders with `order_id`, `customer_id`, `product_id`, `quantity`, `price`
- products with `product_id`, `category_id`, `sku`, `description`
- categories with `category_id`, `category_name`
- customers with `customer_id`, `region`, `industry`

To a database administrator or data engineer, these schemas might be straightforward. But to a business user who just wants to know total revenue by product category, this is not intuitive. In a traditional BI environment, a data modeler would:

• Join these tables logically and create a fact table, `fact_sales`, with key dimensions linked to `dim_products`, `dim_categories`, and `dim_customers`.

• Rename fields to be more user-friendly: `product_category` instead of `category_name`, `total_revenue` instead of `sum(price*quantity)`.

• Apply consistent definitions: ensure that “`revenue`” always means `sum(quantity * price)`, and that product categories are standardized.

When you add a Text-to-SQL solution like Wren AI on top, this modeling work ensures that when a user asks, “What was our total revenue by product category last quarter?” the tool can easily map these terms to well-defined, meaningful fields.

## **2\. Semantic Layers and Precomputed Metrics**

Traditional BI implementations often include a semantic layer or metadata repository. Tools like Looker, Tableau Data Modeling, or Power BI’s Data Model let developers define measures (like revenue, margin, or churn rate) and dimensions (time, product, region) upfront. By doing so, end-users don’t need to understand raw SQL or complex logic — they can drag-and-drop predefined measures and attributes onto a dashboard canvas.

In a Wren AI Text-to-SQL scenario, this semantic layer becomes even more critical. Instead of a dashboard canvas, users have a conversation interface. When they ask for “month-over-month revenue growth,” Wren AI looks to the predefined metric definition and the data model that expresses how month-over-month growth is calculated. Without this, the text-to-SQL system may struggle to correctly infer how to compute growth rates or compare months.

## **3\. Precomputations and Deterministic Datasets**

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In traditional BI, data engineers often precompute certain metrics or create aggregate tables to speed up queries. For instance, you might build a daily summary table that stores revenue by category and day. By doing this, you eliminate the need to recalculate these totals on the fly every time someone runs a query.

With Wren AI, precomputed and deterministic datasets make natural language querying more accurate. Imagine a user asks:

*“Show me the average order value and how it’s changed over the past three months.”*

If you’ve already precomputed a table that contains daily or monthly aggregates of average order value, Wren AI can respond quickly and accurately. If not, the tool will have to dynamically compute the logic from raw transactional data, which increases the risk of ambiguity or errors — especially if the raw data isn’t perfectly modeled.

## **4\. Governance and Consistency**

One of the principles of a sound data model is governance. By standardizing definitions and ensuring consistent naming conventions, you reduce confusion. Traditional BI projects often involve data governance boards, naming conventions, and careful documentation. These best practices don’t disappear with Wren AI — they become more important.

Why? Because when someone asks Wren AI a question, they rely on well-understood terms. If one team uses “revenue” to mean net sales minus returns, and another team uses “revenue” to mean gross sales, the system needs to know which definition to use. Data modeling and governance ensure that such definitions are standardized, creating a single source of truth that Wren AI can reference confidently.

# **Borrowing from Traditional BI: Examples That Apply to Wren AI**

Let’s consider some typical scenarios from a traditional BI implementation and how they map to setting up Wren AI.

## **Scenario 1: Sales Reporting**

*Traditional BI Approach:*

- The data modeler creates a `fact_sales` table that includes `date_key`, `product_key`, `customer_key`, and measures such as `sales_amount` and `quantity_sold`.
- `dim_date`, `dim_product`, and `dim_customer` tables standardize how dates, products, and customers are represented.
- The BI tool references these dimensions and facts to create dashboards showing revenue over time, top products, or top customers.

*Wren AI Approach:*

- Before plugging in Wren AI, ensure your `fact_sales` and `dim_product` tables are stable, well-defined, and contain all the standard metrics your business cares about.
- Precompute logic where beneficial: For example, a `fact_daily_revenue` table that aggregates total sales by date and product category.
- With these datasets in place, when a user asks, “Show me last month’s top five product categories by revenue,” Wren AI can map “last month” to a filter on `date_key`, “revenue” to `sales_amount`, and “product categories” to the `dim_category` table, returning immediate, correct results.

## **Scenario 2: Customer Segmentation and Churn Analysis**

*Traditional BI Approach:*

- Analysts define what “churn” means: a customer who hasn’t purchased in the last 90 days, for example.
- They create a `fact_customer_activity` table with precomputed flags or metrics that identify whether a customer is active or churned each month.
- Reports in the BI tool let users filter customers by churn status, segment them by region or industry, and analyze changes over time.

*Wren AI Approach:*

- Incorporate the churn definition into your semantic model. Precompute a column `churned_customer` as a boolean or a segment label in your data pipeline.
- When a user asks, “How many customers churned last quarter compared to the previous quarter?” Wren AI can look up the `churned_customer` flag and time dimensions to deliver an accurate result.
- Without this predefinition, Wren AI would have to guess what churn means, leading to inconsistent or incorrect results.

## **Scenario 3: Inventory and Supply Chain KPIs**

*Traditional BI Approach:*

- Data engineers build a `fact_inventory` table to track stock levels, reorder points, and backorders.
- A `dim_product` and `dim_supplier` help relate products to suppliers, and a `dim_date` provides a temporal dimension.
- Analysts precompute metrics like “`stockout_rate`” or “`average_lead_time`” in a separate aggregate table.

*Wren AI Approach:*

- Continue to rely on these precomputations and well-modeled tables.
- For example, if you have a table `fact_inventory_agg` that stores daily inventory levels and stockout counts, Wren AI can instantly answer: “What was our stockout rate for supplier X last week?”
- If you didn’t define and precompute “stockout rate” (perhaps it’s `stockout_count / total_items_sold`), Wren AI would have to try inferring it from raw tables, which may lead to confusion.

# **Why Storing Data in a Structured Database Isn’t Enough**

A common misconception is that if your data is already in a relational database, you’re set. **But a raw relational schema is often designed for transaction processing, not for business intelligence.** Tables might be normalized in complex ways or named in developer-friendly terms rather than business terms. Foreign keys and relationships may exist but be unclear to a non-technical user.

Wren AI’s strength is natural language querying — not magically deducing your business rules from cryptic schemas. By transforming raw tables into business-oriented datasets, you reduce the cognitive load on the text-to-SQL engine. When columns and tables have meaningful names — `sales_amount` instead of `col_x56`, `product_category` instead of `cat_desc_var1` — Wren AI can map user intent to data elements more effectively.

Additionally, precomputing logic in your transformation pipeline removes ambiguity. If you know you always measure “last quarter’s revenue” as the sum of sales between the first and last day of the previous quarter, you can store these aggregates in a table keyed by quarter. Wren AI then simply applies filters to the correct dataset. This precomputation ensures self-service users get accurate results without needing to wrestle with definitions or raw data every time.

# **Practical Tips for Getting the Most Out of Wren A**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/676427fc0c13c0c3702ddbc0_0*qqOp2ChLE99tkVA_.png)

## **1\. Start with a Strong ETL/ELT Foundation**

Your pipeline should already be extracting, transforming, and loading data into a well-organized schema. Take the time to rename columns, clean up data anomalies, and join related tables. Just as traditional BI tools benefit from clean, modeled data, so does Wren AI.

## **2\. Implement a Robust Semantic Layer**

If your organization used LookML in Looker, Data Models in Power BI, or semantic layers in Tableau, the same principles apply. Define your metrics, dimensions, and hierarchies in a way that aligns with your business. For instance, store “Annual Recurring Revenue (ARR)” in a separate metric table or tag it as a known measure. Wren AI can then pick up “ARR” easily when users query it.

## **3\. Precompute Common Metrics**

Identify the top queries and metrics your business users frequently ask for. Do they often analyze monthly recurring revenue, average order value, churn rate, or conversion rates? Precompute these in your data pipeline. This could mean creating aggregate tables keyed by time periods, customer segments, or product categories. The result: faster, more deterministic answers from Wren AI.

## **4\. Standardize Business Logic and Terminology**

Data modeling includes enforcing consistent naming conventions and documentation. Make sure you have a data dictionary that explains what each metric and dimension means. This dictionary can guide both technical implementers and serve as a reference when users interact with Wren AI. The more consistent your terminology, the easier it is for Wren AI to interpret queries correctly.

## **5\. Iterate and Refine Based on Feedback**

When you first deploy Wren AI, observe how users interact with it. Are they asking questions that don’t map cleanly onto your model? Maybe you need to add a new semantic definition or precompute a different metric. Just as with traditional BI, the data model evolves as the business changes. Continuously refining your model ensures Wren AI remains accurate and valuable.

# **Transforming Traditional BI into a Natural Language Experience**

![](https://cdn.prod.website-files.com/65f19a32ed7f3c4f5655a8c9/676427fc728e95c59c722319_0*XtxrgcRkG67Ifxtl.png)

Instead of viewing text-to-SQL and data modeling as opposing forces, think of them as complementary. Traditional BI practices — ETL/ELT pipelines, semantic layers, data marts, and aggregate tables — set the stage for Wren AI to deliver on its promise of accessible analytics.

By building on proven data modeling techniques, Wren AI becomes your next-generation BI interface, empowering anyone to tap into curated, well-defined data. Rather than a collection of confusing tables, your analytics environment is a set of rich, meaningful datasets that Wren AI can navigate with ease.

# **Data Modeling Is Alive and More Important Than Ever**

Text-to-SQL solutions like Wren AI have not killed data modeling; they’ve elevated its importance. These new tools rely on the hard work that has always fueled effective BI — clean, well-structured data models, predefined business logic, semantic layers, and precomputed metrics.

- Without data modeling, Wren AI struggles with ambiguous terminology, inconsistent definitions, and unclear relationships.
- With data modeling, Wren AI thrives, translating natural language questions into accurate insights against your carefully prepared datasets.

In other words, data modeling isn’t dead; it’s the essential backbone that allows text-to-SQL interfaces to shine. By combining the wisdom of traditional BI implementations — such as ETL pipelines, semantic layers, and metric definitions — with the cutting-edge capabilities of Wren AI, you can create a seamless, self-serve analytics experience that’s both user-friendly and enterprise-grade.

**Key Takeaways:**

- Text-to-SQL tools excel when built on top of well-modeled, business-oriented data.
- Precompute common metrics, define consistent business logic, and organize data into intuitive schemas.
- Borrow best practices from traditional BI — semantic layers, data governance, and aggregate tables — to give Wren AI the context it needs.
- Continuously refine your data model as business needs evolve, ensuring that natural language querying remains a reliable path to insight.

Ultimately, data modeling remains the blueprint that guides your organization from raw information to actionable knowledge. By leveraging Wren AI on top of that strong foundation, you combine the best of both worlds: the rigor and reliability of traditional BI with the accessibility and convenience of natural language queries.

**If you haven’t tried out the magic of a semantic-first text-to-SQL solution that can transform your analytics experience, we invite you to explore** [**Wren AI**](https://getwren.ai/) **🙌.**

**You can also dive into our open-source offering on** [**Wren AI OSS on GitHub**](https://github.com/Canner/WrenAI) **😍and start building a more intuitive, future-proof data environment today.**

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blog-post_Is Data Modeling Dead with Text-to-SQL How Wren AI Bridges Modern BI and Traditional Practices.png

Is Data Modeling Dead with Text-to-SQL? How Wren AI Bridges Modern BI and Traditional Practices

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