| What it does | AI execution infrastructure that turns mission-critical manual tasks into reliable, paid AI services
| Context gateway that gives coding agents verified codebase knowledge to skip rediscovery and work faster
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| Stage | Not given
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| Founded | Not given
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| US
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| Pricing model | Contact for pricing
| Freemium
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| Pricing | Not given
| Standard: Usage-based pricing with 25 free credits at signup, unlimited repos and run minutes. Enterprise: Outcome-based pricing with all Standard features plus self-hosted licensing, SSO/SAML, audit logs, private runners, and advanced support.
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| Key features | - Job of Work Mapper: identifies valuable processes and maps required outcomes, steps, systems and stakeholders
- Execution Engine: manages multi-week or multi-month workflows with fault tolerance, checkpoints and error recovery
- Built-in stakeholder management: communicates with stakeholders across channels and escalates exceptions
- Intelligence flywheel: captures execution data to benchmark models, improve accuracy and optimise costs
- Small focused AI models in deterministic graph: reduces errors and cost vs single large models
- Fully auditable and explainable execution
- Durable execution engine with checkpoints and automated recovery
| - Verified context injection for coding agents
- Automatic repository baseline generation and validation
- Genome context graph showing dependencies and risk
- DocuSOR MCP for agent context access
- Pull request status checks and gating
- Documentation claim testing for README and runbooks
- Isolated cloud sandbox for validation runs
- GitHub App integration with read-only repository access
- SOC 2 Type 1 compliance
- TLS encryption in transit and at-rest data protection
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| What makes it different | - Handles multi-stakeholder workflows spanning days, weeks or months without requiring core product rebuild
- Partners keep control of brand and customer relationships while using platform for execution
- Usage-based pricing aligns fees with completed outcomes rather than features
- Small focused AI models designed for accuracy and repeatability in high-trust workflows
- Includes mapping stage to validate commercial viability before significant investment
| - 77% faster agent execution compared to manual repository exploration
- Up to 64% lower token costs measured on Claude Opus 5.5
- 100% deterministic validation without LLM/RAG dependencies
- Automatic context sharing across all agents via MCP
- Works with any coding agent through standard MCP protocol
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 12 ● no change since last check | 5 ● no change since last check |
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| Trust Flow (Majestic) | 8 | 4 |
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| Citation Flow (Majestic) | 29 | 17 |
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| Referring domains (Majestic) | 64 | 9 |
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Aguru
DocuSOR