| What it does | From Requirements to Code. Verified.
| Codebase intelligence platform for AI agents and teams
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| Stage | Not given
| Startup
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| Founded | Not given
| 2024
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| Based in | CY
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| Pricing model | Not given
| Freemium
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| Pricing | Startup: Free for up to 3 users and 3 projects. Cloud Starter: $9 per user/month with unlimited users and projects. Cloud Pro: $19 per user/month with unlimited users, projects, 1,000 included AI credits per licensed user per month, and priority support. Self-hosted: Starting at $3,000 per instance/
| Free: 2 public repos, 10 AI answers/month. Pro: $15/month (moving to $20/month in November, or $16/month yearly), includes private repos, auto-sync on every push, security scanning, $5/month AI credits. Teams: $20/seat/month with 3-seat minimum, includes 25 shared repos, shared $5/seat/month credit
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| Key features | - Specification Management: Manage connected requirements, data entities, APIs, interfaces, wireframes, and test cases in a versioned product model with traceable relations and branch-aware revisions
- Change Management: Define each product change through a Change Request that connects goal, affected specifications, project branch, implementation impact, and linked issue
- Architecture & Software Units Blueprints: Define subsystem architecture and software unit blueprints with implementation rules, responsibilities, dependencies, and file boundaries linked to product sp
- AI Assistants: AI Analyst, AI Test Designer, AI Architect, and AI Product Expert for refining specifications, designing tests, developing architecture, and reviewing implementation
- MCP for AI Coding Agents: Give coding agents branch-aware specifications, architecture guidance, software units, file traces, and Change Request context through MCP
- Specification and Code Alignment Verification: Check that code matches specifications and follows architecture rules as the system evolves
- Test Management: Organize test cases, test runs, execution, and result history
- MCP for QA Agents: Give QA agents prepared test runs, cases, steps, and expected outcomes through MCP to record execution results
| - Code health scoring with defect prediction
- Blast radius and change impact detection
- Auto-generated documentation rebuilt on every commit
- Git intelligence: hotspots, owners and coupling detection
- Architecture and decision tracking
- MCP tools for Claude Code and Cursor
- PR bot with change review and test detection
- Security scanning: CVEs, secrets and reachability
- Dead code detection and unused exports
- Change Records with policy enforcement
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| What makes it different | - Preserves product context across tasks, agents, and code changes through a structured, verified product model
- Reduces handoffs and coordination by delegating whole features with connected specifications and implementation scope
- Prevents architecture drift by connecting implementation with subsystem architecture rules, unit guidelines, and component responsibilities
- Built for complex products where a single feature touches connected data, APIs, interfaces, and business rules across multiple repositories and systems
- Keeps product model current as specifications and implementation change using AI assistance and team review
| - Open source under AGPL-3.0, self-hostable with no LLM calls required to build the index
- Every published benchmark includes sample size, corpus and statistical methods
- Code health scores trained on years of real commits and defect fixes, with 0.74 AUC prediction accuracy
- Eliminates need to switch tools: integrates with Claude Code, Cursor, VS Code, GitHub and team dashboards
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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) | 28 ● no change since last check | 10 ● no change since last check |
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| Trust Flow (Majestic) | 18 | 8 |
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| Citation Flow (Majestic) | 34 | 34 |
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| Referring domains (Majestic) | 68 | 244 |
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Reqode
Repowise