Endgame vs RELAI
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Endgame compared with RELAI
| Detail | 
Endgame | 
RELAI |
|---|
| What it does | Context graph for AI-native revenue teams
| The continual learning engine for AI agents
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|---|
| Stage | Established
| Not given
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| Founded | 2021
| Not given
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| Based in | Not given
| Not given
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| Pricing model | Contact for pricing
| Freemium
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| Pricing | Not given
| RELAI Free: $0. RELAI Enterprise: Custom pricing.
|
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| Key features | - One context layer connecting all GTM tools and AI agents
- Agent observability with end-to-end tracing
- Source citations for every answer
- Permission-aware answers scoped to user access levels
- Playbook grounding to enforce business semantics
- Corrections inbox for agent-generated changes
- Integration with Claude, ChatGPT, Slack, Salesforce and other tools
- Open Model Context Protocol (MCP) for any client
- SOC 2 Type II compliance and GDPR/CCPA support
- 140x faster answers via pre-compiled context
| - Capture signals from code, requirements, and production traces
- Build and maintain EvalsWiki as versioned behavioral knowledge
- Find missing eval coverage and unclear expectations
- Generate complete evals with scenarios, environments, and evaluators
- Optimize harness across prompts, tools, workflows, and code
- Multi-objective optimization for quality, cost, latency, and KPIs
- Model selection, migration, and routing with evaluation
- Regression-controlled improvement with evaluation evidence
- Versioned storage in repository with pull requests
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| What makes it different | - 40x lower cost through pre-compiled context vs raw data processing
- 98% answer accuracy with no hallucinations through checked data
- Agent-agnostic: works with any AI tool your team uses
- Never trains on customer data with enterprise-grade security
- Set up within days with forward-deployed engineer support
| - Turns failures into replayable learning environments
- Prevents regressions by keeping previous evals in the loop
- Integrates with existing observability stacks and traces
- Preserves business intelligence versioned in repository
- Adapts harness to model changes without redefining use cases
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| Free plan | Not given
| Not given
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| Open source | Not given
| Not given
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| Platforms | Not given
| Not given
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| Public API | Not given
| Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 48 ▼ −2 (down) since last check | 23 ● no change since last check |
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| Trust Flow (Majestic) | 8 | 13 |
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| Citation Flow (Majestic) | 35 | 25 |
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| Referring domains (Majestic) | 323 | 79 |
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4 details filled in for both companies.
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