| What it does | From Requirements to Code. Verified.
| AI-Native Software Development Extension for VS Code that gives AI structured access to your entire codebase
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| Stage | Not given
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| Founded | Not given
| 2023
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| Based in | CY
| IN
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| Pricing model | Not given
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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/
| Trial: $0 for solo developers with 20k CodeZero tool calls and 100 MB codebase capacity. Starter: $29/user/month with 7.5k tool calls/seat/month and 50 MB/seat capacity. Scale: $39/user/month (most popular) with 13k tool calls/seat/month, 100 MB/seat capacity, and cumulative usage. Enterprise: Custo
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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
| - Git Tree: unified workspace showing all connected repositories with branches, commits and history
- Code Graph: shows system connectivity across modules, files, data and dependencies
- Feature Mapping: traces product features from start through files to data read or written
- Context Graph: repository embeddings and unified graph for AI prompt enhancement
- Auto, Planned, Normal and Coffee Mode for change execution
- AI Chat: conversational interface for code exploration and assistance
- Impact Analysis: understand consequences of changes across codebase
- Access indexed repositories without checking them out locally
- Works with Claude Code, Codex and GitHub Copilot
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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
| - ~Zero hallucination: AI has right code context attached before drafting execution plans
- 60% less tokens and 2x faster than alternative approaches
- Works alongside existing AI coding agents without replacing them
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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 |
| DR (Domain Rating by Ahrefs) | 28 ● no change since last check | 17 ● no change since last check |
| Trust Flow (Majestic) | 18 | 0 |
| Citation Flow (Majestic) | 34 | 16 |
| Referring domains (Majestic) | 68 | 16 |