Chat
Plain-English, for anyone
Business users ask and act. The same governed result they'd get from Natural Language Query.
Platform AI Querying
One question. Chat, SQL, or notebook.
Data Analysis meets every user where they work: the same governed result, reached through whichever interface they prefer.
The answer doesn't change with the interface. A question asked in plain English, written as SQL, or run in a notebook resolves to the same governed result, because all three read the same warehouse, where every record already has a single, trusted identity, and the same verified query plan, inside the same Workspace permissions.
Capabilities
What it does, in four parts.
SQL editor
Full control, for analysts
Schema-aware, editable SQL with query history. Change the query, run it, get the result, against the same warehouse.
Notebook
Python + SQL, for scientists
Jupyter-compatible cells with preloaded schema context and chart output: no environment setup, no kernel management.
One truth
Continuity, not three screenshots
Every interface reads the same relationships and the same verified query plan, so the numbers agree across teams.
One result, three ways
The same answer, whichever interface you prefer.
“Revenue by region this month”, reached through chat, SQL, or a notebook. Switch tabs; the result never changes.
workspace · revenue_by_region verified
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 |
// editable. Run to execute against the warehouse
| region | revenue |
|---|---|
| APAC | $4.2M |
| EMEA | $3.1M |
| AMER | $2.7M |
In [1]: df = Qwry.AI.sql("revenue by region this month"); df.plot.bar()
Out [1]: APAC $4.2M · EMEA $3.1M · AMER $2.7M
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 →Three teams, three tools.
One set of numbers.
Tell us where your analysts, your scientists, and your business users disagree today. We will show you what it looks like when they read from the same warehouse.