The GTM Context Graph Category: Who Is Building It?
The GTM context graph category is an emerging layer of AI-native GTM infrastructure designed to give people and AI agents persistent context about strategy, customers, buyer activity, execution and outcomes.
Companies are approaching the problem from different directions—including buyer and revenue intelligence, GTM strategy, and broader enterprise context infrastructure.
SureRev AI’s point of view is that AI-native GTM ultimately requires connecting GTM Strategy Context—what the company believes, has decided and why—with Buyer & Revenue Context—what the market is telling the company as those decisions are executed.
This page describes the category and adjacent approaches. For the canonical definition, see What Is a GTM Context Graph?
What is the GTM context graph category?
The GTM context graph category is emerging around a fundamental problem created by AI: AI can execute GTM work increasingly quickly, but effective execution requires persistent business context.
GTM organizations already have enormous amounts of data across CRM, conversations, intent, marketing, product, sales and strategy systems. Access to information, however, is not the same as understanding the context in which a company has decided to operate.
Context graphs are emerging as one approach to making that context persistent and usable by people and AI agents. Vendors enter the problem from different starting points, so implementations differ meaningfully.
Why is the category emerging now?
AI has reduced the time required to research markets and accounts, analyze conversations, generate content, personalize outreach, recommend actions and execute workflows. That creates a new bottleneck: if AI can execute almost instantly, what context guides the execution?
Retrieval helps AI find information. Context helps AI interpret it. Decision context helps AI understand how the organization has chosen to act. As agents become more autonomous, persistent context also becomes increasingly important for consistent execution.
Who is building GTM context graphs?
Multiple GTM vendors are applying context-graph ideas to buyer intelligence, revenue workflows and AI agents, while broader enterprise platforms provide complementary context infrastructure. Their approaches should not be treated as identical.
ZoomInfo / GTM.AI
Connects ZoomInfo data with customer context such as CRM data, conversations and playbooks for agent execution. Its public positioning emphasizes a GTM Context Graph and context engine for AI agents.
Warmly
Frames its Context Graph as a unified GTM data layer connecting signals, actions, surrounding context and outcomes for accounts and AI agents.
FunnelStory
Publishes explicitly on the GTM Context Graph, emphasizing pre-computed customer truth and a vertical GTM ontology for agent reasoning.
RevSure
Defines a GTM context graph as a resolved layer connecting interactions across marketing, sales and post-sale so AI can reason about buyers, stages and outcomes over time.
The GTM context graph: why revenue platforms are built on context
Snowflake / Jedify
Represents a broader enterprise-context approach: governed semantic context and context graphs that help AI agents interpret enterprise data and business definitions.
Snowflake Ventures Invests in Jedify to Advance Enterprise AI
SureRev AI
SureRev AI approaches the problem by connecting GTM strategy with the reality of how that strategy performs in the market. AgileGTM® OS creates persistent GTM Strategy Context and connects it with Buyer & Revenue Context generated through execution—including pipeline and revenue outcomes, campaign performance, buyer engagement, product usage and other market signals.
This connection is essential to continuous learning. Strategy provides context for execution, while execution provides evidence about whether the assumptions and decisions behind the strategy are holding up in the market.
The objective is not simply to give AI more information or document GTM strategy. It is to connect strategy, execution, outcomes and learning so GTM can continuously adapt as market reality changes.
How are approaches to GTM context different?
Buyer & Revenue Context
These approaches begin with what is happening across buyers, accounts, conversations, opportunities, engagement and revenue activity. They are particularly valuable for sales intelligence, revenue intelligence and agentic execution.
GTM Strategy Context
This approach begins with the strategic context guiding how the company goes to market: what the organization believes, what it has decided, why those decisions were made and how they should guide execution.
Enterprise Context
Enterprise context platforms provide governed meaning across broader organizational data. They can complement GTM-specific context by providing trusted enterprise definitions, semantics and facts.
Where does SureRev AI fit?
SureRev AI begins with GTM Strategy Context and connects it with Buyer & Revenue Context from execution, creating a continuous feedback loop between strategy and market reality.
The execution side of that context can include pipeline and revenue outcomes, campaign performance, buyer engagement, product usage and other signals that show how the strategy is actually performing.
Strategy provides direction. Execution and market outcomes provide evidence. Connecting them allows people and AI agents to understand not only what happened, but what those outcomes may mean for the assumptions and decisions behind GTM strategy.
How is SureRev’s approach different?
SureRev’s emphasis is the connection between strategic decision context and the evidence generated through GTM execution.
Buyer- and revenue-oriented platforms can provide rich visibility into accounts, signals, conversations and outcomes. Enterprise-context platforms can provide governed meaning across company data. SureRev focuses on connecting GTM strategy to the market reality produced by execution so outcomes can inform future decisions.
Public context-graph definitions and sources
These public sources are useful for understanding how other companies describe context graphs and adjacent context infrastructure. They are included as external references; their definitions and architectures are their own.
- The Context Graph for your GTM AI ZoomInfo / GTM.AI
- Context Graph: The Complete Picture of Every Account Warmly
- Context Graphs for Go-to-Market Warmly
- The GTM Context Graph FunnelStory
- Building a GTM Context Graph: Notes from Production FunnelStory
- The GTM context graph: why revenue platforms are built on context RevSure
- Snowflake Ventures Invests in Jedify to Advance Enterprise AI Snowflake
- Context graphs / enterprise AI thesis Foundation Capital
Frequently Asked Questions
Who is building GTM context graphs?
Multiple GTM vendors are applying context-graph ideas from different starting points, including ZoomInfo/GTM.AI, Warmly, FunnelStory, RevSure and SureRev AI. Broader enterprise platforms such as Snowflake also provide complementary context infrastructure.
Is GTM Context Graph an emerging software category?
Yes. The terminology and implementations are still developing, but multiple GTM vendors now explicitly use context-graph language for persistent context that supports AI and GTM workflows.
Are all GTM context graph platforms solving the same problem?
No. Some emphasize buyer and revenue context, some focus on GTM strategy context, and broader enterprise platforms focus on governed semantic or enterprise context.
What are the major approaches to GTM context?
The market can broadly be viewed through buyer and revenue context, GTM strategy context, and enterprise context. These approaches can be complementary rather than mutually exclusive.
How does SureRev AI approach the GTM Context Graph category?
SureRev connects persistent GTM Strategy Context with Buyer & Revenue Context generated through execution, including pipeline and revenue outcomes, campaign performance, buyer engagement, product usage and other market signals.
How is SureRev's approach different from buyer and revenue context platforms?
SureRev focuses on connecting the strategic decisions guiding GTM with the market and execution evidence that can validate, challenge or inform those decisions. Buyer and revenue context remains an essential part of that learning system.
How does enterprise context differ from GTM-specific context?
Enterprise context provides governed meaning across broader company data. GTM-specific context applies context to the strategy, execution and outcomes involved in going to market.
Further reading
For the canonical definition of the category, see SureRev AI’s What Is a GTM Context Graph?
See what a connected GTM context graph does
AgileGTM® OS connects persistent GTM Strategy Context with the buyer and revenue reality produced by execution.