Authoritative context: the ground truth your AI relies upon
Your AI is inaccurate, irreproducible, and costly because your ground truth is scattered across systems and unreachable by agents. TopQuadrant turns your data, policies, and knowledge into authoritative context: the references, relationships, rules, and processes machines can reason over and people can trust.
Everyone has the data. Nobody has the ground truth.
The same entity is defined five ways across five systems. Reports disagree. Agents cannot reach a definition they can trust, so they fill the gap themselves.
Conflicting definitions
Every system carries its own version of “customer”, “product” or “risk”, and none of them match.
Unreachable by agents
Your ground truth lives in people's heads and scattered systems, in a form no agent can query.
Agents guess
With no ground truth to reason over, models confidently make up the rest. Inaccurate, irreproducible, expensive.
Four things make context authoritative
Authoritative context isn't a prompt or a pile of documents. It's a governed model of your domain, connected to the data behind it. Four things, working together.
What are the core things?
The concepts your business runs on (customers, suppliers, products, employees) each with a canonical identifier, so the same thing is recognized the same way across every system.
How do they connect?
How those entities relate, modelled as ontologies that capture how your business actually thinks. A customer places an order. A supplier ships a product. An employee owns an account.
What's allowed?
Policies and governance enforced directly on the data, so agents stay compliant, traceable and explainable without being told.
How does work get done?
A machine-understandable map of how people and systems interact across your organization.
Tied to your data landscape, so context links directly to the data instead of copying it.
Why your agents need it
The difference between an answer you check and an answer you act on.
Grounded, and provable
Answers resolve against definitions your organization already agreed on and can produce on request.
Permission-aware by default
Access and policy travel with the context, so agents respect them automatically.
Change once, apply everywhere
Update a definition in one place and every downstream app and agent stays consistent.
Your stack stores your data. None of it says what your data means.
Warehouses store, catalogs describe, semantic layers translate for BI. Authoritative context defines what your data means and keeps that meaning correct everywhere your agents reach it.
Warehouses & lakehouses
They store and compute data.
We sit above them and provide the context that makes their data usable by agents.
Semantic layers
Built for structured data and BI queries.
We work across structured and unstructured data, built for agent reasoning.
Data catalogs
They describe and retrieve metadata.
We connect it into a foundation that enforces one consistent view.
Authoritative context
The governed meaning layer the rest of your stack plugs into. We don't store your data or replace what you run. We define what it means and keep that meaning correct everywhere.
One ground truth · governed · provableGraph databases
They store and query connected data.
We're the layer above: modelling, governance and lifecycle in production.
MDM
Golden records in isolation.
We connect master data to ontologies, metadata and logic.
AI knowledge platforms
Document search for individuals.
We provide the machine-readable context agents reason over across the business.
How authoritative context gets built
Modeled once on open W3C standards and connected to the systems your data already lives in. No rip and replace. Built and governed on the TQ Data Foundation.
Point at your data
Start from what you already know.
Point it at your databases, catalogs, repos and documents. It surfaces the models and terms already living inside them.
Generate with AI
Let the platform propose the first version.
AI turns what it found into draft models your team can react to. The hard part, a first structure, is already done.
Make it authoritative
Your experts make it authoritative.
Subject-matter experts review, refine and sign off. Human approval turns the draft into governed context.
Wire it into agents
Serve it where AI is built.
Deliver approved context to your agents, copilots and apps through MCP and SDKs, living context, not a static export.
Keep it true, automatically
Keep it true, automatically.
It watches for drift across your systems, flags what changed and proposes updates, so authoritative stays authoritative.
Authoritative context FAQs
What teams ask when they stop guessing and start governing.
The governed ground truth of your enterprise: the references, relationships, rules and processes connected to your data, that your agents and your people reason from instead of guessing.
Inferred context is auto-generated and plausible, and unconfirmed. Authoritative context has been confirmed and agreed by the people who own it, then governed. Inference is a guess. Authority is agreement.
AI gets you a fast first draft. It cannot make context authoritative on its own, because authority requires human judgment about which definition applies where. AI proposes, your experts confirm.
A source of truth usually means one clean copy of a record. Authoritative context adds the relationships, rules and processes around it and makes all of it machine-readable, so an agent can reason over it rather than just read it.
MDM produces golden records in isolation. Authoritative context connects those records to ontologies, metadata, rules and processes, then activates them into your agents.
No. Context is modelled on open standards and linked to the systems your data already lives in. It links to the data instead of copying it.