Build GTM agents on a context graph

Connector sprawl slows every GTM build: a dozen sources in, a dozen AI tools out, each making sense of the data alone. Endgame puts one cited context graph in the middle, so you can build with confidence.

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Handle
Case study

One builder at Handle gave leadership the account dashboards it had wanted for two years

Executive views3built via Endgame MCP
Systems6connected in one graph
Sales cycles6–9 mofrom 12 to 18 months
“Because your MCP server is accessible in my Claude workflows, I can build systems that know when to query Endgame for intelligence, when to branch into other systems, and how to assemble everything into exactly what a stakeholder needs.”
Ryan Vanshur · VP of GTM Intelligence and AI Solutions
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Build what sets your GTM apart

Not the plumbing. The foundation is already joined, resolved, and current.

One graph behind all the places you and your team work with AI

Wired directly, each AI tool needs its own connector to every source, and you maintain all of them. Connect the tools to the graph once over MCP, and they all answer from the same data.

Cursor
Claude
ChatGPT
Slack
Your app
Notion

Skip the integration work and start building the agent

Syncs, schema mapping, deduping, and keeping it all fresh are already handled in the graph. You iterate on the agent’s logic and workflows, not the pipework that feeds it.

app.endgame.io/settings/integrations

Integrations

  • Chorus
  • Confluence
  • Gong
  • Google Drive
  • Looker
  • Notion
  • Salesforce
  • Slack
  • Snowflake
  • Zoom

Accuracy that holds up when you automate across every account

A wrong fact in one chat is one bad answer. In an agent running across your whole book, it’s hundreds. The graph settles what’s true before any agent reads it.

Who’s the champion at Meridian Bank?
Without Endgame
  • Agent APriya ShahOut of date: left in June
  • Agent BDana WhitfieldWrong person: the sponsor
With Endgame
  • Agent AMarcus BellCited to the Aug 12 call
  • Agent BMarcus BellCited to the Aug 12 call

Keep token costs down as you build, iterate, and run at scale

Agents read the compiled facts instead of the raw calls and records, about 50× less text per run. Run them on all your accounts, as often as you like.

See more in the Monte Carlo case study →
Text the model reads per run
4.8 KB~1.2K tokens
249 KB~62K tokens
3 MB~750K tokens

Built for the enterprise

Enterprise-grade security, compliance, and control.

  • SOC 2Type II
  • GDPRCompliant
  • CCPACompliant

Built for the people building on it

Agent-ready tools, every run traced, and answers scoped to whoever asked.

Claude Code
Claude CodeWelcome to Claude!
❯ /mcpTools for claude.ai Endgame36 tools↑6.tell_endgame7.get_preferences8.get_graph_citations9.get_graph_entities❯10.get_graph_field_catalog

Tools built for agents

Orient with an index, search cited facts, and write back.

Agent session6 calls · 24s
  1. 9:04:11search_graph_factsfind the renewal terms
  2. 9:04:18get_graph_personwho owns the renewal
  3. 9:04:22get_graph_citationscheck the source of the date
  4. 9:04:26update_salesforcewrite the corrected date
  5. 9:04:31tell_endgamenote the change for the team
  6. 9:04:35list_my_accountsfind the next renewal

Every agent run traced

See every call an agent made and what it read, so when an answer looks wrong you can find the step that caused it.

Access controlService account
  • Row-level filtersInherited from Salesforce
  • Role hierarchyInherited from Salesforce
  • Sharing rulesInherited from Salesforce
  • Service-account scopeInherited from Salesforce
  • Field-level securityInherited from Salesforce

Permissions you don’t have to build

Scope is inherited from your CRM, so an agent never sees more than the person it runs for.

Build GTM agents on a context graph

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Questions & answers

Answers to what teams ask us most.

Over the MCP server at app.endgame.io/api/v1/mcp, a REST API, or the CLI. Claude, Claude Code, ChatGPT, and Codex connect with one command and OAuth; automations and service accounts use API keys. Every call returns facts with their citations, already resolved to the right account, people, and deals.

Direct connections make an agent re-read raw records on every run, and each run rebuilds the account differently. Monte Carlo tested both on live accounts: the graph runs returned identical, cited briefs every time, while raw runs disagreed about who was in the buying committee, and read 50 to 600× more tokens to get there.

Yes. Call the API or MCP server from any workflow tool on a trigger or a schedule, and write the results back to CRM fields, Slack, or your own app. At Monte Carlo, Nooks automations check a contact with Endgame before it enters a sequence.

No. Connect the sources where they live — Salesforce, Gong or your call recorder, Slack, email, calendar, docs, and your warehouse — and Endgame compiles them into the graph and keeps it current. Nothing you build depends on a copy you have to maintain.

Service accounts carry the same row-level permissions as people, so an automation sees only what its owner could. Every session is traced end to end, SAML/SSO and audit logs are standard, we never train on your data, and Endgame is SOC 2 Type II compliant.