| What it does | From Requirements to Code. Verified.
| Run Python automations on a schedule or when events arrive, controlled from your AI assistant
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| Stage | Not given
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| Founded | Not given
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| Based in | CY
| IN
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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: 250 runs/month, 420K input + 50K output AI tokens. Starter: $9/month or $90/year, 2,000 runs/month, 2M input + 400K output tokens. Plus: $39/month or $390/year, 7,500 runs/month, 8M input + 1.6M output tokens. Team: $99/month or $990/year, 25,000 runs/month, 20M input + 4M output tokens. Scale
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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
| - Schedule Python scripts to run from every 5 minutes to monthly on any timezone
- Receive webhooks and run automations on external events
- Store persistent state between runs with JSON values and counters
- Call any HTTPS API with saved secrets and connected accounts
- Send emails and post to Slack from automations
- Built-in Gemini AI model for text classification and summarization
- Full run logs with secrets masked and failure notifications
- Use CLI or MCP to set up and manage automations
- Approve all actions including turning on, running now, and deleting
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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
| - Keeps user fully in control with approval required for all actions
- Never exposes API keys to the assistant, keys stay in the dashboard
- Works with multiple AI assistants including ChatGPT, Claude and Cursor
- One command works for both Node.js and Python installations
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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 | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 18 | 0 |
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| Citation Flow (Majestic) | 34 | 0 |
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| Referring domains (Majestic) | 68 | 0 |
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Reqode
ClickRuntime