Platform Data Foundation

One question. Every system. An answer you can check.

Ask something that spans a database, a spreadsheet, and a document archive that nobody designed to talk to each other, and get an answer with the SQL that ran, the tables it touched, and a confidence score attached, not a fluent, confident, wrong paragraph you have to take on faith.

cross-source query executed

Question

Which customers have open invoices but no recent orders?

Generated SQL

SELECT c.name
FROM customers c
JOIN invoices i ON i.customer_id = c.id
LEFT JOIN orders o ON o.customer_id = c.id
WHERE i.status = 'open' AND o.id IS NULL

Answer

source attribution confidence score row count

Most AI-on-your-data tools will attempt a cross-source answer. Few will show their work. The distinction that matters isn't whether the answer sounds right, it's whether you can trace it back to an executed query against a warehouse whose relationships were already discovered and scored, versus a language model inferring a join on the fly and hoping it holds.

SQL-executed, not LLM-inferred

A question is converted into SQL that traverses the discovered relationship graph. The join was mapped and scored before the question was ever asked, not invented in response to it.

Source attribution on every answer

Which tables, which sources, which rows. An answer without a visible source is treated as a defect, not a feature.

Confidence score, visible

The same scoring that underlies the relationship graph surfaces at the point of answer, so a low-confidence join produces a visibly lower-confidence result rather than the same fluent tone as a high-confidence one.

Row count, always shown

An answer built on three matching rows looks different from one built on thirty thousand. Both are shown as what they are.

This is what Relationship Discovery and Ingestion & ETL were building toward, and it inherits the same access boundaries as Governed Warehouse: a role that can't see a table can't get an answer derived from it, no matter how the question is phrased.

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

Personal Care & FMCG 10 min read

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

Pharma Distribution 3 min read

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 case

Browse all insights

Connectors

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.