| What it does | From Requirements to Code. Verified.
| Stop fixing what your AI just wrote.
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| Stage | Not given
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| Founded | Not given
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| Based in | CY
| AE
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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: ₹0 forever with 15 agent runs per day and 1 concurrent session. Starter: ₹99/month for first 2 months, then ₹899/month with 40 agent runs per day and 2 concurrent sessions. Pro: ₹999/month for first 2 months, then ₹3,999/month with 200 agent runs per day and 5 concurrent sessions.
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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
| - Plans features from rough ideas into scoped tasks
- Understands and maps codebases
- Writes code from natural language descriptions
- Debugs errors and traces failures
- Generates focused tests for behavior and edge cases
- Assists with deployment steps
- Monitors issues using runtime context
- Agent mode for automated execution
- Reviewable execution and permission controls
- Works with most modern languages and frameworks
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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
| - Full engineering loop support from intent to production
- Plans, builds, debugs, tests, deploys, and monitors
- Routes prompts efficiently to reduce wasted execution
- Explains decisions so you learn while shipping
- Works with existing codebases and new projects
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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 ▲ +1 (up) since last check | 20 ▼ −1 (down) since last check |
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| Trust Flow (Majestic) | 18 | 3 |
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| Citation Flow (Majestic) | 34 | 21 |
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| Referring domains (Majestic) | 68 | 16 |
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Reqode
OxCode