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
Ask in plain English. Get SQL you can check.
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
What it does, in four parts.
Verified SQL out
Every answer shows its query
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
Cross-source questions just work
Questions resolve against the relationships Qwry.AI already discovered, so a question spanning three systems needs no join written by hand.
Governed by role
It only reads what you can see
The generated query never touches a table the asker isn't cleared for: the same boundary enforced in the Workspace.
Try it
A question in, the query out.
Pick a question. Read the answer, the results, and open the exact SQL that produced it.
LLM Chat · workspace verified
How many active customers do we have?
You have 12,431 active customers, up 4.2% this quarter.
| metric | value |
|---|---|
| active customers | 12,431 |
| new this quarter | +498 |
| definition | v3 · endorsed |
View SQL
SELECT count(*) FROM customers WHERE status = 'active' AND last_order_at > now() - interval '90 days';
Which suppliers shipped late in Q3?
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 |
View SQL
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;
What is revenue by region this month?
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 |
View SQL
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
Use cases & field notes from the data trenches.
Customer playbooks, technical deep-dives and field notes from deployments into messy enterprise estates.
Technical
JOIN dms_secondary ON erp.sku_code
→ 94% match · conf 0.94
→ 94% match · conf 0.94
Contextual data in personal care manufacturing & distribution: the context between systems is the missing asset.
Every personal care manufacturer generates data at every stage of its value chain, and cannot join it. How automated relationship discovery turns five to eight disconnected systems into cross-source answers in minutes, not days.
Read the white paper Use Case
ERP Secondary Sales GST
One view across distributors, billing, and compliance: how pharma distribution stops flying blind.
Secondary sales in spreadsheets. Distributor stock self-declared. Billing in the ERP. Compliance records as PDFs. How Qwry.AI connects them into one governed view, and what becomes possible when it does.
Read the use caseConnectors
Reads from the systems you already have.
Databases, warehouses, files and the apps you run: connect what you already have, don't migrate. Every source lands in one governed graph, with 200+ on the roadmap.
Don't see your connector? Tell us your stack. We'll scope it.
Reach out →Ask your data a question.
Check the answer yourself.
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.