The ROI of embedding GenBI: build vs. buy.
What an enterprise-grade text-to-SQL context layer actually costs to build in-house, and a line-item worksheet to estimate your savings with numbers you can put in a budget doc.
Wren AI vs. building in-house
Skip the build. Keep the roadmap.
10–19
engineering-months
to recreate an enterprise-grade embedded GenBI stack
2–4
quarters
to reach a first customer in-house, versus days with Wren AI
~0.5
FTE every year
for model churn, schema drift, evaluations, and security upkeep
Illustrative enterprise-grade scope from the worksheet. Replace it with your own numbers.
- 01
What building in-house actually costs
The six subsystems behind production GenBI: text-to-SQL accuracy, context layer, governance, charts, UX, and permanent maintenance.
- 02
Build vs. buy, side by side
Engineering-months and ongoing upkeep against embedding Wren AI: one iframe snippet, a REST API, or MCP.
- 03
The ROI worksheet
Line-item calculator with illustrative examples and blank columns for your own numbers.
- 04
What ships in days, not quarters
White-label Embedded Threads, the Embedded AI API, and MCP, all governed by one context layer.
Get the ROI worksheet
Fill out the form and we'll take you straight to the PDF.
Free PDF · 2 pages