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
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Free PDF · 2 pages