Cross-database detection
Finds that orders.customer_id in MongoDB refers to customers.id in
MySQL, and that a column in an Excel export lines up with a table in PostgreSQL. Most tools
stop at "same database."
Platform Data Foundation
The join nobody wrote is the answer nobody trusts
Every enterprise data problem eventually turns out to be the same problem: two systems hold pieces of the same fact, and nobody resolved them against each other. The real bottleneck was never the model. It's identity resolution. Qwry.AI does that work first, with a confidence score attached to every edge, before any model is allowed near your data.
relationship graph discovered
confirmed candidate
Overview
Most "data readiness" work is really unmapped-relationship work wearing a different name. Dashboards get built on top of joins someone half-remembers writing eighteen months ago. Chat-with-your-data tools guess at foreign keys the way an intern would. Neither approach survives contact with an auditor, or a wrong answer in front of a customer.
Confidence scoring, not a guess
Every candidate relationship is scored on name similarity, data overlap, and pattern strength. High-confidence and low-confidence matches are both shown. Nothing is silently assumed.
Cardinality detection
One-to-one, one-to-many, many-to-many, detected automatically, not configured by hand in a modelling tool three weeks after go-live.
Living, not static
Every resync re-evaluates the graph. A relationship discovered on day one doesn't quietly go stale as schemas drift.
This graph is what makes Cross-Source Joins possible at all: a query spanning three systems only works because the relationship between them was already discovered, scored, and confirmed. It's also what feeds the Governed Warehouse layer's table-level permissions, since access control on a relationship-mapped warehouse means something different than access control on a pile of disconnected tables.
Insights
Use cases & field notes from the data trenches.
Customer playbooks, technical deep-dives and field notes from deployments into messy enterprise estates.
Blog
baselineproof of valueoutcome
A 30-day proof of value, not a chatbot demo
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.
Read the article Technical
triggercandidatesevidencereview
Investigate the reason behind the alert
Without investigation, an organisation risks automating the distribution of unexplained alerts. The result is more noise, not better decisions.
Read the article Blog
signal context evidence next step
An alert is not an insight
More alerts do not create more value. Without context and prioritisation, they simply create another queue of work.
Read the articleConnectors
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 →