Platform Data Ontology Engine
One agreed definition for every entity.
Across your systems, the same thing goes by different names and means different things to different teams: a field called "region" meaning something different three systems over. The ontology engine resolves that once; then every query, workspace, and agent reads from the same definition.
4 → 1
Names resolved to one entity
0.99
Top join confidence, verified
v3
Endorsed definition, versioned
100%
Verified SQL behind every answer
The Problem
Two reports. Both correct. Still contradicting.
"Customer" in your CRM is not the same field as "client" in billing or "account holder" in a contract. When every source carries its own private vocabulary, no tool can give a consistent answer. Scroll the story; the panel follows along.
Today
Same question, two answers.
Sales counts anyone with an order in 60 days. Finance counts billing status. Both reports are technically correct; they disagree by 665 customers. Nobody can say which one to trust, so the meeting argues about the number instead of the decision.
The engine proposes
One definition, drafted from your data.
The ontology engine reads all four sources, finds that customer, client, account holder, and end user point at the same party, and proposes a single endorsed definition of "active". Your team reviews and confirms it: the people who know the business, not a black box.
After endorsement
Every tool reads the same line.
From then on both tools resolve "active customer" against definition v3. The numbers match because the meaning matches. When the business changes the window, the definition changes once: visibly, with a version and a name attached.
Sales · LLM Chat
"How many active customers?"
13,096
orders in 60d
Finance · SQL Editor
"How many active customers?"
12,431
billing status
Δ 665 customers: both correct, no shared definition
"active customer": no agreed definition found
Sales · LLM Chat
"How many active customers?"
N/A
awaiting definition
Finance · SQL Editor
"How many active customers?"
N/A
awaiting definition
Engine proposal under review by Data Governance
proposed · active = status('active') AND last_order > now()−90d
synonyms · customer = client = account holder = end user Sales · LLM Chat
"How many active customers?"
12,431
definition v3
Finance · SQL Editor
"How many active customers?"
12,431
definition v3
Identical answers: both resolved through entity#customer @v3
endorsed v3 · active = status('active') AND last_order > now()−90d
endorsed by A. Rao · Data Governance · 12 Mar How It Works
Operational, not decorative.
Four properties keep the ontology alive. Open a card to see each one in practice.
Business glossary Speaks your language, not column names Maps the words your teams actually use ("active customer", "net revenue", "open order") to the tables and fields that hold them.
Human-endorsed Proposed by the engine, confirmed by people The engine drafts entities, attributes, and synonyms from your sources. Your team reviews, refines, and endorses each one.
Versioned Meaning changes on the record Every definition keeps its history and its endorser: a shift in meaning is a visible decision, not silent drift that breaks last quarter’s numbers.
Compounding Every new source lands on agreed ground Connect a new system and the engine proposes how its vocabulary maps to definitions you already endorsed; coherence grows instead of fragmenting.
One Definition, Live
The ontology is not documentation. It answers.
Every query Qwry.AI runs reads from the endorsed definition, with the verified SQL one tap away. Run the question and watch it resolve.
Press Run the question to watch a live resolution.
status='active' AND last_order > now()−90d
You have 12,431 active customers, up 4.2% this quarter.
Where it sits in the platform
The ontology holds the meaning; the Enterprise Context Graph holds the connections. 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 →Build your business ontology
on your own data.
Talk directly to the team who built the engine. Bring your sources; we will show you the definitions it surfaces.