Two analysts. Same metric name. Different numbers. Not a bug — every metric hides 25-40 decisions nobody explicitly made. QueryLens finds them all. → Score: Paste any definition. Get a score 0-100 across 7 categories. AI coaching improves it one question at a time. → Hidden Decisions: Reveal every implicit decision with severity and variance impact. → Knowledge Graph: See how metrics connect. Find blind spots that repeat. Free to try — no signup needed for your first 2 scores.

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Fine-tune any LLM in minutes, with one prompt

Every metric hides 25-40 decisions nobody explicitly made.

Take churn. Sounds simple — cancelled customers ÷ total customers.

But underneath that formula:

- Do trial users count as churned?

- Calendar month or rolling 30 days?

- What about paused subscriptions?

- Failed payments — immediate churn or grace period?

That's 4 of 37 decisions in a typical churn metric. Each one changes the number by 40-200%. Most teams have documented maybe 5 of the 37. The other 32 are assumptions buried in someone's SQL query.

QueryLens finds them all.

Paste any metric definition → get a score from 0-100 across 7 decision categories → AI coaching improves it one question at a time. Watch the score climb from 22 to 91 as each hidden decision becomes explicit.

Also: Hidden Decisions analysis reveals every implicit decision with severity ratings and variance impact.

Knowledge Graph shows how metrics connect across your ecosystem.

Free to try — no signup needed: askquerylens.com

We'd love honest feedback from the PH community. What metric would you score first?