Give AI context
Endgame compiles every call, email, meeting, and playbook your team produces into one living context graph that people and agents can act on.
NVIDIA
nvidia.com · Santa Clara
Expanding fast, but single-threaded since August
Top-5 customer; the March renewal and a Q4 expansion move as one $3.0M decision.
Champion Marcus Lee left in August. Priya Shah stepped in and opened a path to CFO Elena Ruiz — still the only thread, so the sponsor ask is on Thursday's agenda.
Security review 19 of 24, due Thursday. Rollout at 71% weekly active, +240 seats scoped for Q4.
✓ Overview + workstream agents · 12 sources · updated 12m ago
Leading GTM teams trust Endgame
More accurate, faster, lower cost,
and trusted context for agents
When you compile context ahead of time, agents get a lot more performant.
interactions with agents
- Answers served
- 260k
- Agent sessions
- 11.5k
- Facts extracted
- 255M
Every answer shows its sources.
Each fact links back to the file it came from, so anyone can verify it in one click.
Answers stick to your data.
Checked and organised before anyone asks. No guessing, no invented facts.
Instant answers.
Answering is a lookup: 140 to 330 times faster than reading the raw files.
A fraction of the cost.
About 40 times less text processed per answer. Reliable answers, far cheaper.
Measured from real product usage in September 2026, and carried forward since at that month's rate.
One brain behind every surface your team works on
Claude, ChatGPT, Slack, your CLI, the apps you build: same answer, same citations, from the same context.
Your team already works here — so the context should too. Every answer comes back grounded and verified.
A single source of trust
One place to see, correct, and govern everything your agents know — cited to source, grounded in your playbooks, and scoped to your permissions.
Agent observability
Every session is traced end to end: which agents ran, what they did, what they read, and what they touched.
Corrections inbox
Agents fix what they can and queue the rest for review. You approve or decline every change, so the graph never drifts on its own.
Tell Endgame
Spot something wrong? Push a correction in one line and it propagates through every page, answer, and agent at once.
Playbook grounding
Endgame learns your best practices, your definitions, and your semantics and applies them in every answer.
Champion went quiet after the Mar 3 pricing call[1] and the security review is the last open item.[2]
Source citations
Every fact links back to the exact call, email, or record it came from, so anyone can verify any answer in seconds — nothing on faith.
Permission-aware answers
Answers respect the access you've already structured — a rep sees their own book, a manager sees every account their team owns, nothing more.
Built for the enterprise
We never train on your data. Enterprise-grade security, compliance, and control.
SAML / SSO
Single sign-on through your existing identity provider.
Encrypted
Encrypted in transit and at rest.
Audit logs
A reviewable record of every action an agent takes.
Integrations
Salesforce, Gong, Slack, Teams, email, and more.
Open protocol
Any MCP client connects — Claude, ChatGPT, or your own.
Admin visibility
Team and per-user activity, visible to your admins.
Customers feel the impact
Customers see it where it counts — win rate, pipeline generated, and rep productivity.
- ACV increase
- 70%
- Win rate growth
- 9%
- Systems connected
- 6
Ryan Vanshur built an always-on intelligence layer so Handle can query any strategic account, surface risks and stakeholder gaps, and align leadership on next steps.
Read the full case study →- AI Adoption
- 80%
- across the GTM team
- Tools Replaced
- 5+
- consolidated into one
- Time Saved
- 10-20%
- per rep, per week
Monte Carlo's Co-founder & COO, Jordan Van Horn, started the year with a question that would reshape everything—and achieved 80% AI adoption across their entire GTM team.
Read the full case study →- Research Time
- Minutes
- prev hours
- Time to Exec
- 20 min
- handed to an exec
- Team Productivity
- 10x
- across many teams
BetterUp's teams started with prototypes, but scaled into production with Endgame, unlocking myriad use cases across many teams.
Read the full case study →- Research Time
- 5 min
- from 30-60 min
- Tools Connected
- 4+
- into one unified view
- Context
- Unified
- at the account level
Hex didn't need more information. They needed the context from their existing systems connected and actionable when it mattered most.
Read the full case study →Stay on the frontier
Read what our team has to say on applied AI research and what we're learning.
2027: The agent sprawl paradox
A team of 8 now has 240 agents running. None of them talk to each other. Why deploying more agents can make you less productive — and what to build first.
What 30,000+ AI interactions reveal about how GTM teams actually work
Behavioral data from real go-to-market professionals, not surveys or self-reports
GTM economics: How curious revenue leaders decrease marginal expenses
A few years ago when a CRO wanted to understand why deals were stalling, it meant asking an analyst to sift through Salesforce, marketing data, call recordings, and email threads to assemble a narrative that felt complete.
The Prototype Gap: How to build Endgame yourself
Most Endgame demos end with the same question: This is cool. But couldn't we just build this ourselves in GPT or Claude?
Get started
Two ways in: build on the context layer yourself, or have our FDEs build it for you.
Talk to us