Platform Enterprise Context Graph
One view of how your data actually connects.
Enterprise data is scattered across databases, documents, and spreadsheets that nobody designed to talk to each other. The Enterprise Context Graph resolves it into a single, governed map of how every entity relates: one trusted identity, wherever it lives.
crm.customers billing.invoices 0.99
Evidence
- value overlap · 11,842 / 12,431 keys matched (95.3%)
- format agreement · both UUID v4, no collisions
- cardinality · 1:N, consistent across 18 months
Lineage
- Discovered relmap v1.3 · scan 12 Mar 04:12
- Scored deterministic · overlap + format + cardinality
- Endorsed A. Rao · Data Governance · 12 Mar
billing.invoices contracts.msa_parties 0.71
Evidence
- name similarity · 1,604 / 2,100 parties matched (76.4%)
- no shared key · joined on normalised party name
- conflict · 63 parties map to more than one account
Lineage
- Discovered relmap v1.3 · document scan 12 Mar
- Scored below endorsement threshold (0.85)
- Flagged awaiting review · not used in query planning
crm.customers support.tickets 0.94
Evidence
- declared constraint · tickets.requester_id → customers.id
- referential integrity · 847k rows, 412 orphans (0.05%)
- confirmed by · sampling 10k rows
Lineage
- Discovered schema read · constraint declared upstream
- Scored deterministic · integrity check
- Endorsed A. Rao · Data Governance · 12 Mar
ops.pricing_sheet billing.invoices 0.68
Evidence
- header inference · SKU column detected, no schema
- value overlap · 88% of SKUs resolve to a line item
- volatility · sheet edited by hand, 14 revisions this quarter
Lineage
- Discovered file scan · ops/pricing_2026.xlsx
- Scored below endorsement threshold (0.85)
- Flagged source is hand-maintained · review advised
The Problem
The map that always gets rebuilt from scratch.
Analytics platforms answer questions about data that has already been organised. AI assistants answer questions about data they assume is already connected. Neither does the organising itself. Scroll the story; the panel follows along.
Today
The map lives in people’s heads.
Customer records sit in one database, transactions in another, contracts as documents, pricing in a spreadsheet someone updates by hand. Nothing captures how they relate, so teams rebuild that map from memory every time they need an answer.
Discovery
The connections are found, and scored.
Qwry.AI reads the estate and proposes each relationship with a confidence signal and the evidence behind it. Scoring is deterministic rather than a guess from a language model: a strong connection looks different from a weak one for reasons you can check. Anything uncertain is surfaced for review rather than assumed.
One map
Every consumer reads the same map.
The queries your analysts run, the workspaces your teams build, and the agents that act on your data all draw on one shared understanding of how your estate connects. That is why answers stay consistent across tools and across teams.
- crm.customers billing.invoices unknown
- billing.invoices contracts.msa_parties unknown
- crm.customers support.tickets unknown
Rebuilt from memory, every time, and it leaves when people do
- crm.customers billing.invoices 0.99
- billing.invoices contracts.msa_parties 0.71
- crm.customers support.tickets 0.94
Each edge carries a score and the evidence behind it
- crm.customers billing.invoices endorsed
- billing.invoices contracts.msa_parties in review
- crm.customers support.tickets endorsed
Queries, workspaces, and agents all read this map
What It Is
One queryable map across every source.
The Enterprise Context Graph is a single, queryable map of every entity in your data estate and every relationship between them. Customers to orders. Orders to invoices. Invoices to the supplier records buried in a document. It holds the connections across structured databases, unstructured files, and everything in between.
It is not a diagram you draw once and file away. It is a live asset that reflects the current state of your data and keeps pace as your sources change.
How It Works
An asset, not a project.
Four properties make the graph something that appreciates. Open a card to see each one in practice.
Cross-source Across every source, not just within one The connections that matter rarely live inside a single system. A customer in one database is referenced by an order in another, by an invoice in a document, and again in a pricing spreadsheet. The graph connects relationships across all of them: structured or unstructured, database or file.
Discovered Discovered, not hand-drawn Qwry.AI discovers relationships automatically and presents each one with a confidence signal and the evidence behind it. Scoring is deterministic rather than a guess from a language model. Anything uncertain is surfaced for review rather than assumed.
Lineage Lineage you can follow Because the graph records where every connection came from, you can trace any relationship back to its origin: the sources it was found in and the decision that confirmed it. When a number looks wrong, you can follow it back to where it started rather than argue about it in a meeting.
Compounding Built to compound Connect a new database and its relationships are discovered and added. Bring in a document set and its entities join the map. Every source you add makes the graph more complete, and everything built on top inherits that completeness. The asset appreciates; it does not depreciate.
One Graph, Every Consumer
Everything in Qwry.AI reads the same map.
The queries your analysts run, the workspaces your teams build, and the agents that act on your data all draw on one shared understanding of how your estate connects. That is why answers stay consistent across tools and across teams: they are all reading the same map.
This is not a technical detail. It is the difference between an organisation that argues about numbers and one that trusts them.
Where it sits in the platform
The graph is the layer everything else stands on: it holds how your entities connect. The Data Ontology Engine defines what they mean. Verified execution reads from both, so every answer carries its source and its logic.
Insights
Use cases & field notes from the data trenches.
Customer playbooks, technical deep-dives and field notes from deployments into messy enterprise estates.
→ 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 paperOne 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 →See the Enterprise Context Graph
against your own data.
Talk directly to the team who built the relationship discovery engine. Bring your sources; we will show you the graph it produces.