From Requirements to Code. Verified.
For teams building with AI coding agents like Codex, Cursor, or Claude Code. Keep architectural drift in check and code aligned with product intent.
Explore your product specifications
Start from FieldDesk’s dashboard and explore Requirements, Data Entities, User Interfaces, or API Operations.
Problems Reqode Focuses On
Product context gets lost. Architecture drifts. Handoffs multiply. AI agents lose autonomy.
AI Loses the Product Model
Decisions get lost in prompts and conversations, so every session needs the same context again. Agents miss related APIs, data, and UI behavior.
Architecture Drifts as the Codebase Grows
Local changes drift from architectural rules, component responsibilities, and subsystem boundaries that the agent cannot see.
Product Changes Need Too Many Handoffs
You break a product change into database, API, and UI tasks, repeat the context, and connect the results yourself. Each step needs another prompt, another handoff, and more of your attention.
How Reqode Solves It
Reqode gives teams a connected, branch-aware product and implementation model, exposes focused context to AI agents, and verifies work against that source of truth. Use the model to guide the next feature, then use verification and reviewed changes to keep it current.
- Product model Requirements, APIs, data, UI, architecture, linked code
- Feature specifications Reviewed changes with connected context
- Agent implementation A defined change guided by the model
- Verification & reviewed updates Check the result and resolve differences
- Product model Updated context for the next change
Capabilities
Specification Management
Architecture and Unit Catalog
MCP Context for AI Coding Agents
Structured AI Assistants
Specs to Code Verification
Change, QA, and Issue Traceability
Start with one product area
Structured, Verified Memory for AI Agents
Reqode gives AI coding agents a structured, verified memory of your product: requirements, specifications, architecture rules, software units, and change history. Through MCP, agents retrieve focused, branch-aware context for each task, while Reqode's verifiers keep that source of truth aligned with the code.
Frequently Asked Questions
Use an MCP-compatible agent such as Codex, Cursor, or Claude Code. Reqode supplies product and implementation context while the agent works in your coding environment. See MCP for Coding Agents.
Yes. Start with one relevant area, connect its repository context, and review the specifications that describe it. You can expand the model as you make further changes.
Your team can write them directly or use AI Analyst to clarify a feature and propose changes. Review the proposed diff and apply the accepted result.
Your team chooses the intended behavior, accepts specification changes, reviews the implementation and evidence, and decides what to release.
AI verifiers inspect specifications, architecture guidance, and linked implementation within their defined scope. Test runs record execution results. Review both kinds of evidence; a completed check is not a guarantee that a feature is correct.
Reqode connects implementation with subsystem architecture rules, unit guidelines, and component responsibilities. AI verifiers check the selected code against that guidance and record findings for your team to review. Explore architecture checks.
Review the intended behavior before choosing a correction. If the product decision has changed, your team can use AI assistance to prepare a specification update, review and apply it, then check the implementation against the revised specification. Explore how to resolve a mismatch.
