Source attribution
Which tables, which rows
Every answer names the warehouse tables it queried and the relationships it relied on. An answer without a visible source is a defect.
Platform AI Querying
Every answer, with its receipts.
Verified Answers pairs each result with a confidence score, a row count, the source tables, and the SQL that produced it.
Ask a model to reason across data that was never resolved to one identity and it won't tell you it's confused: it gives you a fluent, confident, wrong answer. Qwry.AI returns the same sentence attached to the query that produced it, the tables it read, and how confident it is. It is the same result you'd reach in Natural Language Query or a notebook on Data Analysis, never a black box, and always inside the boundaries of your Workspace.
Capabilities
What it does, in four parts.
Confidence, visible
A weak join looks weak
The same deterministic scoring behind the graph surfaces at the answer, so a low-confidence result reads differently from a high-confidence one.
Row count, always
Three rows or thirty thousand
An answer built on a handful of rows looks different from one built on the whole table. Both are shown as what they are.
Executable SQL
Reproducible on demand
The query is preserved, timestamped, and replayable, reached the same way through Data Analysis.
The difference
Same question. One shows its work.
Both answer “Which suppliers shipped late in Q3?” Only one lets you check it.
Typical AI chatbot
Which suppliers shipped late in Q3 across all our warehouses?
Around two dozen suppliers had late shipments in Q3. The worst offenders were Supplier A, Supplier B, and Supplier C.
no source · no confidence · no query
Qwry.AI · verified answer
Which suppliers shipped late in Q3 across all our warehouses?
23 suppliers shipped late at least once. Top three by volume: 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;
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 →Stop trusting answers.
Start checking them.
Bring us a number your teams argue about. We will show you the query behind it, the tables it came from, and how confident the platform is in the join.