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Endgame

Vendor: Endgame

Endgame is a context graph platform that processes calls, deals, and playbooks into structured knowledge for GTM agents, delivering cited answers across Claude, Slack, and workflow tools via MCP and API integrations.

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linkedin email Enterprise sales teams Revenue operations GTM agent deployments Account-based marketing #context graph #GTM agents #AI-powered sales intelligence #MCP integrations #cited answers #sales knowledge system

Overview

Endgame is a knowledge system for enterprise sales that builds a context graph from real files — calls, emails, documents — to power AI agents and human reps with grounded, cited answers. It organizes data ahead of time so answers are 140–330x faster and ~40x cheaper than raw AI reading. Every answer links back to its source file for verification. The platform integrates with agent platforms like Claude, ChatGPT, Slack, CLI, Zapier, n8n, Make, and Tray via MCP and API. Forward-deployed engineers help map use cases and build custom connectors. Trusted by leading revenue organizations, Endgame runs at production scale on operated infrastructure with real compliance.

Key Features

Context graph from real sales data

Processes every call, deal, email, and playbook into structured, queryable knowledge that AI agents and humans can act on.

Cited, verifiable answers

Every answer links directly to the source file (call, email, document) so teams can verify any fact in one click. 98% of answers match real data.

Instant answer lookup

Pre-organized data enables answers 140–330x faster than AI reading raw files live.

Lower AI compute cost

Organized context graph processes ~40x less text per question, drastically reducing inference cost.

One intelligence, every surface

Same grounded answers delivered in Claude, ChatGPT, Slack, terminal, or autonomous workflows — consistent context everywhere.

MCP and API integrations

Integrates with Claude Managed Agents, ChatGPT, Zapier, n8n, Make, Tray, and other agent platforms via MCP and API.

Forward-deployed engineering

Dedicated engineers map use cases, build custom connectors, and stand up workflows in production.

Production-scale infrastructure

Runs on end-to-end operated infrastructure with real compliance; engineers get paged on issues so customers don't.

Pros & Strengths

  • ✓
    Grounded, verifiable AI answers: Every answer cites its source file, eliminating hallucinations and enabling instant verification.
  • ✓
    Massive speed and cost advantage: 140–330x faster answers at ~40x lower compute cost vs. raw-file AI approaches.
  • ✓
    Universal agent compatibility: MCP and API integrations let any GTM agent (Claude, ChatGPT, Zapier, n8n, Make, Tray) pull from the same context graph.
  • ✓
    Hands-on deployment support: Forward-deployed engineers build custom connectors and workflows, reducing time to value.
  • ✓
    Enterprise-grade reliability: Operated infrastructure with real compliance and 24/7 engineering on-call.

Cons & Tradeoffs

  • ⚠
    Pricing not publicly detailed: Pricing structure requires contacting sales or requesting a pricing deck; no self-serve tiers visible.
  • ⚠
    Enterprise-focused complexity: Built for production GTM agent deployments at scale; may be overkill for small teams or simple use cases.
  • ⚠
    Requires data integration setup: Value depends on connecting call recordings, emails, CRM, and documents — initial setup effort required.

Known Limitations

  • • Pricing details are not publicly available; requires sales engagement.
  • • Optimized for enterprise GTM teams deploying agents at scale; less suited for small teams.
  • • Full value realization requires integrating multiple data sources (calls, emails, CRM, docs).

Pricing & Plans

Model: custom

Pricing is customized for enterprise deployments; a pricing deck is available upon request. No public self-serve tiers or free plan/trial indicated.

✓ Free Plan ✓ Free Trial

Pricing verified: 2026-08-20

Editorial Info

Last Reviewed: 2026-08-20

Verified Sources & Research

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