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.
Where AI delivery breaks down
People and AI Lose Product Context
Product decisions get scattered across chats, documents, and code. Team members and AI agents repeatedly reconstruct the same context, missing connections between requirements, 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
Built for products with more moving parts
Reqode is designed for projects where a single feature touches connected data, APIs, interfaces, and business rules across your system.
-
Prototype
One repository
A few data entities
One main user flow -
Focused product
One app and its backend
Dozens of data tables
Several user roles -
Connected product
Multiple apps or services
Shared data and API contracts
Roles and cross-app workflows -
Complex system
Multiple repositories
Hundreds of data tables
Many roles and integrations
- Prompt engineering: prototypes through focused products.
- Open-source spec frameworks: focused products into complex systems, overlapping with prompts and Reqode.
- ReqodeConnected context across the system: from focused products through complex systems.
Explore your product specifications
Start from FieldDesk’s dashboard and explore Requirements, Data Entities, User Interfaces, or API Operations.
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.
Start with one product area
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.
