AI app for it and development · no coding needed
Requirements-to-deployment AI delivery workspace
Reduce handoff loss between requirements, code and deployment while keeping senior review and client approval.
Made for: Software product teams and agencies turning requirements and designs into shipped code
What it does for you
The problem
Requirements, specs, tickets, reviews and billing live in separate rented tools, so context is lost between design, build and deployment.
What it gives you
Reviewed, tested, deployed features with fixed-price tickets
What you give it
Product briefsscreen mockupsrepository contextbacklog items
Build your own version of Omniflow, MindyGem and more
One app with what these 5 AI tools do, yours to keep and change: Omniflow, MindyGem, Baseline Core, SonOf, Clears.
Everything these tools do, in one app
- AI workflow automation Automates business processes and tasks using machine learning that adapts to user behavior.Found in Omniflow
- Third-party integrations Connects with popular external applications, APIs, and tools like Figma and Swagger.Found in Omniflow, MindyGem
- Automated document generation Generates complete documents and technical specifications from product questions or uploaded screen mockups.Found in MindyGem
- User story decomposition Converts PRDs or briefs into actionable feature suggestions and detailed user stories with acceptance criteria.Found in MindyGem
- AI-driven specifications Uses machine learning to detect UI elements and generate accurate technical requirements from design inputs.Found in MindyGem
- Unified collaboration platform Stores all documents, requirements, and specifications in one place where team members can comment, set statuses, and plan.Found in MindyGem
- Version control for requirements Maintains version control for project requirements to minimize misunderstandings.Found in MindyGem
- Real-time discussions Enables real-time discussions within the platform.Found in MindyGem
- Document management Provides efficient document management.Found in MindyGem
- Skills as markdown files Defines skills as markdown files with a YAML manifest that tell agents which context and references to load.Found in Baseline Core
- Prebuilt resource collection Includes a prebuilt collection of skills, frameworks, and reference files to jumpstart workflows.Found in Baseline Core
- CLI scaffolding Creates a consistent repo structure (skills/, context/, frameworks/, AGENTS.md) for reuse and sharing.Found in Baseline Core
- Tool-agnostic approach Works alongside a variety of AI coding and assistant tools rather than locking you to one provider.Found in Baseline Core
- Context layer Lets you narrow what gets fed into a skill or indexes repositories and pages so outputs stay focused.Found in Baseline Core, Clears
- Free codebase audit Maps the existing product and identifies what's ready to build.Found in SonOf
- Fixed-price tickets Provides tickets with a fixed price per story point, locked before development starts.Found in SonOf
- Senior engineer review A senior engineer reviews both the initial plan and the final pull request.Found in SonOf
- Production-gated billing Charges apply only when code is deployed, not for work in progress or staging.Found in SonOf
- Staging preview Provides a staging preview for client approval, with a redo-or-refund policy if the feature isn't right.Found in SonOf
- Independent test execution Writes unit tests for each story, runs them, and executes a build to confirm the code passes before a PR is created.Found in Clears
- CI integration and auto-fix Integrates with the pipeline to fix issues automatically and runs its own tests in parallel.Found in Clears
- Agent-based code review A separate agent with broader context reviews the PR without being biased by the implementation approach and posts fixes directly on the PR.Found in Clears
- Risk and complexity scoring Assesses expected risk and complexity of a task before spinning up an agent session.Found in Clears
- Parallel execution Allows teams to run multiple tasks on the backlog concurrently rather than sequentially.Found in Clears
- Customizable dashboards Provides customizable dashboards and real-time analytics for monitoring performance.Found in Omniflow
- Task scheduling and prioritization Schedules and prioritizes tasks based on predictive algorithms.Found in Omniflow
- Collaboration tools Enables team communication within workflows.Found in Omniflow
How it works, step by step
- Automate recurring delivery tasks from observed team behavior
- Connect Figma, Swagger, repositories and external APIs
- Generate technical specifications from product questions or uploaded mockups
- Decompose PRDs into user stories with acceptance criteria
- Detect UI elements and derive technical requirements from designs
- Store requirements, specs and comments in one shared workspace
- Version requirements to prevent misunderstandings
- Support real-time discussion on each document
- Manage documents with statuses and ownership
- Define skills as markdown files with a YAML manifest
- Ship a prebuilt collection of skills, frameworks and reference files
- Scaffold a consistent repo structure (skills/, context/, frameworks/, AGENTS.md)
- Work alongside multiple AI coding and assistant tools
- Narrow context per skill and index repositories and pages
- Audit the existing codebase and flag what is ready to build
- Price tickets per story point before development starts
- Route the initial plan and final pull request to a senior engineer
- Bill only when code is deployed, not for work in progress
- Provide a staging preview with a redo-or-refund policy
- Write and run unit tests and a build before opening a PR
- Integrate with CI, fix issues automatically and run tests in parallel
- Have a separate agent review the PR with broader context and post fixes
- Score risk and complexity before starting an agent session
- Run multiple backlog tasks concurrently
- Provide customizable dashboards and real-time analytics
- Schedule and prioritize tasks with predictive algorithms
- Keep team communication inside each workflow
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 Requirements-to-deployment AI delivery 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 Requirements-to-deployment AI delivery 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 links5 KB
- questions.mdQuestions to answer before you build2 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 criteria13 KB
- demo/index.htmlThe working demo on sample data201 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 handoff loss between requirements, code and deployment while keeping senior review and client approval. For software product teams and agencies turning requirements and designs into shipped code, convert product briefs, screen mockups, repository context and backlog items into reviewed, tested, deployed features with fixed-price tickets and production-gated billing. The benefit is a testable hypothesis, measured through accepted story points per delivery hour and rework after deployment; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect product briefs, screen mockups, repository context and backlog items, then follow this sequence: 1. Audit the existing codebase and flag what is ready to build. 2. Generate specifications and decompose them into user stories with acceptance criteria. 3. Price tickets per story point and score risk and complexity. 4. Run agent sessions in parallel with narrowed context. 5. Write and run unit tests and a build before opening a PR. 6. Route the plan and PR to a senior engineer and a separate review agent. 7. Deploy to staging for client approval, then bill on deployment. Resolve uncertain cases with qualified reviewers, approve reviewed, tested, deployed features with fixed-price tickets, and measure accepted story points per delivery hour and rework after deployment against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Senior engineer review and client staging approval remain human; final deployment and billing authorization stay with the buyer. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license compliance and deployment permissions. Senior engineers and clients approve substantive changes and release scope. One repository, one design source and one deployment target; senior engineer review and client staging approval 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, one design source and one deployment target; senior engineer review and client staging approval remain human. Implement one approved input format, a bounded representative case set and the first two task modules: audit the existing codebase and flag what is ready to build; generate specifications and decompose them into user stories with acceptance criteria. Support the remaining modules with operator review: price tickets per story point and score risk and complexity; run agent sessions in parallel with narrowed context; write and run unit tests and a build before opening a PR; route the plan and PR to a senior engineer and a separate review agent; deploy to staging for client approval, then bill on deployment. 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, design files, API specifications and deployment targets. Cloud source control, CI pipelines, issue trackers and staging environments. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Requirements and design intake, Delivery board with agent sessions, Review and deployment. Use a project gallery, a central ticket and spec canvas, and a right-hand panel for context, skills and comments. Let users compare plan versions side by side. Display draft, in review, tested, staged and deployed states. Provide a client staging preview link with comments anchored to the relevant feature. Make the task-specific outcome reviewed, tested, deployed features with fixed-price tickets visible beside its evidence, review state and value baseline.
