A context graph is the operating memory your AI agents run on. It keeps them acting on what is current, what is permitted, and what your organization has actually decided — with every action traceable to its source. We build it on top of the data and systems you already have.
You are not buying a diagram. You are buying AI agents that can be trusted with real decisions: acting on what is true now, staying inside your rules, and showing their work.
Move past chatbots that summarize. Your agents take the next step — draft, decide, route, resolve — on facts that are current and permitted, so you can put them on work that matters.
Every action links back to the exact facts, decisions, and policy behind it. When a regulator, auditor, or customer asks why, the answer is one click away, not a reconstruction.
Grounding agents in current, governed context is what stops confident-but-wrong actions before they reach a customer, a filing, or a payment.
A context graph captures the three things a plain knowledge base leaves out: when a fact holds, why a decision was made, and what an agent is allowed to do.
Time-bounded context, so an agent acts on today's truth, not a definition that quietly expired.
The decisions, exceptions, and precedents behind each outcome, so agents follow how your organization actually operates.
Access, compliance, and approval rules built into the graph, so agents stay inside the lines automatically.
We build the context graph on top of the systems and data you already run. No rip-and-replace. Scoped tight around the decisions it has to support, so you see value on one decision before you widen it.
We connect the documents, records, and systems you already have, and structure them around the decisions your agents need to make.
We capture temporal validity, decision traces, and your governance rules as first-class parts of the graph — the layer generic tools skip.
Your agents query the context graph before they act, grounding every step in current, governed facts — with a trace back to the source.
A context graph stands on its own — you do not need a knowledge graph first. The two pair well, though: a knowledge graph maps what exists across your organization, and we build those too. Take a context graph on its own, or both together.
Already have a knowledge graph? We connect the context graph to it, so your agents draw on both.
A context graph delivers on its own, on the data you already have. Start with the one decision you want your agents to own.
Every document and decision you add strengthens the same structure, so the system gets more useful over time, not more brittle.
A 30-minute conversation about the decisions you want your agents to own, and what a context graph would take. No deck, no pitch.