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Trusted answers from your data — for everyone who needs them.

Finance asks for last quarter's revenue. A business user asks why a metric moved. An analyst asks how loan disbursals trended. They all use AI now — and they all get different answers.

Stemma plugs into the AI tools your team already uses — Claude, ChatGPT, Cursor — and grounds every answer in your warehouse, BI, code, and docs. Same data, same definitions, citations back to source.

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What it looks like

A grounded answer comes back with the definition it used, the sources it cited, and how fresh the data is. A walkthrough video is in the works — here's the shape of it.

Claude stemma context
What was net revenue last quarter, by region?
Resolving: net revenueFinance definition·3 candidate sources

Q3 net revenue was ₹148.2 Cr, up 11.4% QoQ. North and West regions drove the lift; South was flat.

snowflake.finance.gl_summary· refreshed 2h agoqlik · Finance Revenue dashboard· v4.2docs/definitions/net-revenue.md· owner: Finance
Sales team has a different definition of net revenue (excludes refunds in a different window). Stemma flagged the ambiguity and used the Finance owner's definition for this answer.

What Stemma is

Connect

Wire up your warehouse, BI tool, code, and docs. Stemma watches them continuously — no one-shot crawls, no stale catalog.

Stitch

Schemas, business definitions, and tribal knowledge get reconciled into a single source of truth. Ambiguities surface, instead of silently corrupting answers.

Serve

Plugs into the AI tools your team already uses — Claude, ChatGPT, Cursor — so every answer comes back grounded, with citations to the source.

Built for everyone who asks the data a question

You shouldn't need to be on the data team to get a trustworthy answer. Stemma sits behind the AI tools your people already use — so finance, business users, product, sales, and leadership all get answers grounded in the same source of truth, with the same definitions.