Agents are only as good as the data you feed them. Endgame processes every call, deal, and playbook into structured knowledge every agent can act on.
Helix Analytics is a SaaS data engineering platform for the modern data stack — customers run Helix for change-data-capture, pipeline orchestration, and warehouse observability. Strategic account in the enterprise segment, expanded twice in the last 18 months.
Trusted by the world's leading revenue organizations
answers Endgame has delivered to sales teams
served in eight weeks of measurement
Every answer shows its sources.
Endgame builds every answer from your real files, the actual calls, emails, and documents. Each fact links straight back to the file it came from, so your team can verify anything in one click. An AI answering from scratch can't promise that.
Measured against reality: is the cited source a real file? It always is.
Answers stick to your data.
Endgame checks and organizes your data before anyone asks a question, so answers stay true to what is actually there. 98 of every 100 answers match your real data, no guessing, no invented facts.
Measured against reality: is the answer true to your actual data?
Instant answers.
Because Endgame reads and organizes your data ahead of time, answering a question is a quick lookup. Looking up a fact is 140 to 330 times faster than an AI that has to read the raw files from scratch every time it is asked.
Compared with an AI with no graph, reading the raw sources live.
A fraction of the cost.
AI cost scales with how much text it has to read. Endgame's organized data is compact, so answering a question processes about 40 times less text than an AI churning through the original calls and emails. Reliable answers, far cheaper.
Compared with an AI with no graph, reading the raw sources live.
Every number on this page is measured from real product usage. Not estimated. Not projected.
Every agent and every person reads from the same context graph, in Claude, in Slack, in the terminal, or inside an autonomous workflow. Same grounded answer, wherever the question gets asked.
Reps and non-technical teams ask the questions they already ask. Endgame returns cited answers and ships finished artifacts in the same flow.
One cited answer in the channel where the work already happens. AE, CSM, manager, all on the same source of truth, same citations.
Ops teams scaffold apps and workflows straight from the terminal, built on the same context layer the rest of the org sees.
MCP and API integrations into Claude Managed Agents, Zapier, n8n, Make, and Tray. Every node pulls from the same context.
Most agents fan out across a dozen tool calls per question, fetching, re-stitching, and re-applying your methodology every time. Endgame agents make one call, because the reasoning was already done upstream.
Every fact has already been reasoned against your methodology and how you sell, so each query is fewer tokens, more consistent answers, and higher accuracy.
Nothing is reasoned ahead of time. The work doesn't disappear, it moves to every single query.
The agent has to rebuild context and re-apply your methodology from scratch on every single ask, so you get token bloat, drifting answers, and lower accuracy.
From security to scale, Endgame is built for the demands of GTM teams in production. Real engineers, real architecture, real compliance.
Forward-deployed engineers map your use cases, build custom connectors, and stand up workflows in production.
SOC 2 and ISO 27001 certified. Encryption in flight and at rest. Granular role-based access control.
Runs at production scale on infrastructure we operate end-to-end. Our engineers get paged when it doesn't, so yours don't.
Route across OpenAI, Anthropic, and open-source models. Pick the right one for the job, with no lock-in.
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 →Blog posts, product launches, and machine repo additions, fresh from the lab.
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.
Behavioral data from real go-to-market professionals, not surveys or self-reports
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.
Most Endgame demos end with the same question: This is cool. But couldn't we just build this ourselves in GPT or Claude?
Connect your sources, encode your methodology, and ship every agent and every rep on the same context graph. The one that actually understands how you sell.
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