A deep look at each capability — from metrics definitions to visual lineage to AI verification.
Define every metric once — name, formula, definition, gotchas, and example queries — all in one place. When someone asks AI "what's our churn rate?", it uses your exact definition, not a guess.
Column stores monetary values in cents. Divide by 100 to get dollars before displaying or aggregating.
Rows are not physically deleted. Filter by deleted_at IS NULL to exclude soft-deleted records.
Table contains test accounts. Filter by is_test = false for production data.
Every data warehouse has landmines — columns that look straightforward but aren't. Gotchas are reusable warnings that you tag to columns so AI handles them correctly every single time.
Your data doesn't exist in a vacuum. Contextary lets you document company-wide context — fiscal year boundaries, naming conventions, source system quirks, and the decisions that shaped your data model.
See where your data comes from and what depends on it. An interactive graph lets you trace any table upstream to its source or downstream to every dashboard that depends on it.
Don't just trust the answer — verify it. Ask Contextary a question in plain English, see the query it generates, run it against your real data, and confirm the results make sense before sharing them.
Data knowledge shouldn't live in one person's head. Your whole team can contribute annotations, metrics, and gotchas — building a shared understanding that compounds over time.
Push your documented context back into your warehouse as table and column comments. Your BI tools — Looker, Tableau, dbt — pick up the descriptions automatically.
Contextary serves your data knowledge over the Model Context Protocol (MCP) — an open standard supported by Claude, Cursor, Claude Code, and a growing list of AI tools. Your context is always available, no matter which tool your team prefers.