AI app for product development · no coding needed
Developer-ready spec and execution workspace
Reduce spec-to-working-code cycles while keeping the plan and its evidence current.
Made for: Product and engineering teams turning scattered product context into specs that AI coding agents can execute
What it does for you
The problem
Product ideas and scattered context do not become structured, developer-ready specs, so AI coding agents execute ambiguous work and documentation goes stale.
What it gives you
Reviewed, developer-ready specs and atomic tasks linked to code modules
What you give it
Ideasrepositoriesmeeting recordingstool signals from SlackJiraConfluence
Build your own version of CasDoc, Deep Work Plan and more
One app with what these 3 AI tools do, yours to keep and change: CasDoc, Deep Work Plan, Radiq.
Everything these tools do, in one app
- AI spec generation Uses AI to create structured specs from ideas, repositories, or meeting recordings.Found in CasDoc, Radiq
- Structured developer-ready specs Produces specs formatted for developers to implement directly.Found in CasDoc, Deep Work Plan, Radiq
- Living documentation updates Keeps specs current as development progresses to reduce stale documentation.Found in CasDoc
- Collaborative workspace Provides a shared space for real-time editing and collaboration among team members.Found in CasDoc, Radiq
- Context bundle export Exports structured context bundles formatted for AI coding assistants.Found in CasDoc
- Plan-as-source-of-truth Stores the plan as a file in the repository that drives execution.Found in Deep Work Plan
- Atomic tasks with acceptance criteria Breaks work into small tasks each with clear acceptance criteria.Found in Deep Work Plan
- Validation gates Runs executable commands to verify task completion before marking done.Found in Deep Work Plan
- Resumable state Records task status durably so work can resume after context resets or agent swaps.Found in Deep Work Plan
- Agent-agnostic design Works with any AI coding agent because the plan is plaintext in the repo.Found in Deep Work Plan
- Drift control Re-runs validation gates against the current repo state to catch and surface failures.Found in Deep Work Plan
- In-run refinement Allows editing, reordering, or splitting tasks mid-execution without losing completed work.Found in Deep Work Plan
- Knowledge graph mapping Maps customer evidence to code modules and dependencies using code analysis.Found in Radiq
- Signal ingestion Consolidates context from tools like Slack, Jira, and Confluence into a single source.Found in Radiq
- IDE task push via MCP Pushes structured tasks directly into IDEs like Cursor, VS Code, and Windsurf.Found in Radiq
- Operational safeguards Uses PM-validated pattern templates and automatically decays stale mappings on codebase sync.Found in Radiq
How it works, step by step
- Generate structured specs from ideas, repositories or meeting recordings
- Format specs for direct developer implementation
- Update living documentation as development progresses
- Provide a shared workspace for real-time editing and collaboration
- Export context bundles formatted for AI coding assistants
- Store the plan as a file in the repository that drives execution
- Break work into atomic tasks with acceptance criteria
- Run executable validation gates before marking tasks done
- Record task status durably for resumable work
- Keep the plan plaintext and agent-agnostic
- Re-run gates against current repo state to catch drift
- Allow editing, reordering or splitting tasks mid-execution
- Map customer evidence to code modules and dependencies
- Consolidate context from Slack, Jira and Confluence
- Push structured tasks into IDEs via MCP
- Apply PM-validated templates and decay stale mappings on codebase sync
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Developer-ready spec and execution workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Developer-ready spec and execution workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links3 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data202 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce spec-to-working-code cycles while keeping the plan and its evidence current. For product and engineering teams turning scattered product context into specs that AI coding agents can execute, convert ideas, repositories, meeting recordings and tool signals into reviewed, developer-ready specs and atomic tasks linked to code modules. The benefit is a testable hypothesis, measured through accepted tasks per spec hour and rework after agent execution; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect ideas, repositories, meeting recordings and tool signals, then follow this sequence: 1. Generate structured specs from ideas, repositories or meeting recordings. 2. Break work into atomic tasks with acceptance criteria. 3. Run executable validation gates before marking tasks done. Resolve uncertain cases with qualified reviewers, approve reviewed, developer-ready specs and atomic tasks linked to code modules, and measure accepted tasks per spec hour and rework after agent execution against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate specs and tasks for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository layout and one agent protocol; final architecture and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, code ownership and usage permissions. Engineering owners approve substantive spec changes and merge scope. One repository layout and one agent protocol; final architecture and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository layout and one agent protocol; final architecture and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate structured specs from ideas, repositories or meeting recordings; break work into atomic tasks with acceptance criteria. Support the third module with operator review: run executable validation gates before marking tasks done. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned repositories, authorized meeting recordings and permitted tool sources. Cloud code storage, issue trackers, IDE task push via MCP and documentation destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Context intake and evidence, Spec and task board, Validation and drift view. Use a project list, a central spec editor with linked task cards, and a right-hand panel for sources, acceptance criteria and comments. Let users compare spec versions side by side. Display draft, in review, validated and drifted states. Provide a repository-linked view of the plan file and its gate results. Make the task-specific outcome reviewed, developer-ready specs and atomic tasks linked to code modules visible beside its evidence, review state and value baseline.
