| What it does | AI model of your product's business logic accessible to people and AI agents
| The continual learning engine for AI agents
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| Stage | Not given
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| Founded | Not given
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| Based in | GB
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| Pricing model | Freemium
| Freemium
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| Pricing | Free: 10,000 starting credits plus 1,000 per month. Engineering: $29 per active developer per month with 30,000 pooled credits each month. GitMir Vibe: $9 per person per month. Cloud Compute: $99 per project per month. MeetAI: $49 per project per month. GoBots: $99 per venture per month. Market: $49
| RELAI Free: $0. RELAI Enterprise: Custom pricing.
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| Key features | - Builds features, rules and customer paths and keeps them current with every push
- Shows what each commit changed in the business logic, not only in the code
- Provides change impact analysis before merge showing what could break
- Checks PRDs and specifications against the product
- Delivers understanding to AI agents over MCP
- Runs locally on your machine with GMM Local
- Finds risky patterns and problems in the product
- Enterprise deployment inside your security boundary
- Integrates with Claude Code, Cursor, Codex and VS Code
- MCP access for agents with 1-10 credits per answer
| - 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 | - Nearly 3× fewer errors on Benchmark 001 compared to alternatives
- 8.4K tokens per question instead of 27.4K for competitors
- Results published and measurable; method is proprietary
- Reduces AI cost per completed agent action from $0.35 to $0.08
| - 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
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 27 ● no change since last check | 23 ● no change since last check |
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| Trust Flow (Majestic) | 3 | 13 |
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| Citation Flow (Majestic) | 28 | 25 |
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| Referring domains (Majestic) | 50 | 79 |
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GitMir
RELAI