The Context Graph
What a context graph actually is, what it looks like on a real account, and why GTM AI can’t run without one.
What is the Context Graph?
A context graph is everything you know about a company. It centralizes first-party internal data (think CRM records, calls, emails, meeting notes), as well as external signals (think funding events, champion moves, tech stack changes).
Individually, each of these is a fragment. Together, they become one coherent, current view of the account.
What does a Context Graph look like?
This is the visual representation of Uber’s Context Graph in ZoomInfo. At the center of this sits the CRM record. Around it: every call, every email, every parent and subsidiary, every intent signal, every job posting, every funding round, all resolved to the same account.
You won’t find this view anywhere else.
Open your CRM and see for yourself. Uber probably exists as three or four different records under three or four different names, with no hierarchy connecting them. Open up your data vendors and you’ll see a multitude of conflicting signals.
This view is different. This is a unified reference data layer.
Why GTM AI can’t run without a Context Graph
An autonomous agent is only as intelligent as the context layer feeding it. Unlike a coding agent, which runs over a codebase that is self-contained, machine-readable and fully accessible inside a single repository, go-to-market agents face a fragmented reality.
The context an agent needs is scattered across CRM records, calls, and emails. Worse, the systems of record themselves are often unreliable: duplicate accounts represent the same company multiple times, and entities are matched incorrectly.
Then there’s everything your CRM doesn’t know. A new executive joins. A champion change. Or a business acquisition. An account starts showing buying signals.
Without a way to resolve all of that into a single understanding of an account, AI isn’t operating on reality. It’s operating on fragments of it.
Can’t anyone build a Context Graph?
Almost no one. Not without an unreasonable amount of engineering time and budget behind it.
You need to know when two records are the same company, when similarly named companies aren't, how subsidiaries roll up to parents, who works where today, who worked there before, and how all of that changes over time.
This is what we have spent the last two decades building; resolving companies, people, relationships and signals into a continuously updated view of the business world, so your teams don’t have to build that context from scratch.
