Plain-English in
Ask the way you'd ask a person
No SQL, no BI training, no saved-report backlog. A question typed in ordinary language becomes an executed query.
Platform AI Querying
Natural Language Query turns an ordinary question into an executed query against AI-ready data, and shows the query behind every answer.
A business user types a question the way they'd ask a colleague. Qwry.AI resolves it against the identities already established in your context graph, generates SQL, runs it, and returns a result, with the query one tap away. Nothing is inferred that isn't backed by an executed query you can inspect on Verified Answers, and every question runs inside the permissions of your Workspace.
Capabilities
Plain-English in
No SQL, no BI training, no saved-report backlog. A question typed in ordinary language becomes an executed query.
Verified SQL out
The exact SQL sits one tap under each answer. Open it, read it, run it yourself. Detailed further on Verified Answers.
Grounded in your graph
Questions resolve against the relationships Qwry.AI already discovered, so a question spanning three systems needs no join written by hand.
Governed by role
The generated query never touches a table the asker isn't cleared for: the same boundary enforced in the Workspace.
Try it
Pick a question. Read the answer, the results, and open the exact SQL that produced it.
You have 12,431 active customers, up 4.2% this quarter.
| metric | value |
|---|---|
| active customers | 12,431 |
| new this quarter | +498 |
| definition | v3 · endorsed |
SELECT count(*) FROM customers WHERE status = 'active' AND last_order_at > now() - interval '90 days';
23 suppliers shipped late at least once. Top three: Supplier A (47), Supplier B (31), Supplier C (28).
| supplier | late |
|---|---|
| Supplier A | 47 |
| Supplier B | 31 |
| Supplier C | 28 |
SELECT s.name, count(*) AS late FROM shipments sh JOIN suppliers s ON sh.supplier_id = s.id WHERE sh.delivered_at > sh.promised_at AND sh.quarter = 'Q3' GROUP BY 1 ORDER BY 2 DESC;
APAC leads at $4.2M, followed by EMEA ($3.1M) and AMER ($2.7M).
| region | revenue |
|---|---|
| APAC | $4.2M |
| EMEA | $3.1M |
| AMER | $2.7M |
SELECT region, sum(amount) AS revenue
FROM orders
WHERE date_trunc('month', created_at) = date_trunc('month', now())
GROUP BY 1 ORDER BY 2 DESC; Insights
Customer playbooks, technical deep-dives and field notes from deployments into messy enterprise estates.
A demo proves a question can be asked. A proof of value establishes whether the right sources, rules and evidence produced an answer that changed a decision.
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Read the articleConnectors
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Don't see your connector? Tell us your stack. We'll scope it.
Reach out →Tell us what you hold and what you have never been able to ask across it. We will show you the question running against your own estate.