Requirements-to-deployment AI delivery workspace

For

Software product teams and agencies turning requirements and designs into shipped code

Solves

Requirements, specs, tickets, reviews and billing live in separate rented tools, so context is lost between design, build and deployment.

Delivers

Reviewed, tested, deployed features with fixed-price tickets

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce handoff loss between requirements, code and deployment while keeping senior review and client approval.

1. Automate recurring delivery tasks from observed team behavior.
2. Connect Figma, Swagger, repositories and external APIs.
3. Generate technical specifications from product questions or uploaded mockups.
4. Decompose PRDs into user stories with acceptance criteria.
5. Detect UI elements and derive technical requirements from designs.
6. Store requirements, specs and comments in one shared workspace.
7. Version requirements to prevent misunderstandings.
8. Support real-time discussion on each document.
9. Manage documents with statuses and ownership.
10. Define skills as markdown files with a YAML manifest.
11. Ship a prebuilt collection of skills, frameworks and reference files.
12. Scaffold a consistent repo structure (skills/, context/, frameworks/, AGENTS.md).
13. Work alongside multiple AI coding and assistant tools.
14. Narrow context per skill and index repositories and pages.
15. Audit the existing codebase and flag what is ready to build.
16. Price tickets per story point before development starts.
17. Route the initial plan and final pull request to a senior engineer.
18. Bill only when code is deployed, not for work in progress.
19. Provide a staging preview with a redo-or-refund policy.
20. Write and run unit tests and a build before opening a PR.
21. Integrate with CI, fix issues automatically and run tests in parallel.
22. Have a separate agent review the PR with broader context and post fixes.
23. Score risk and complexity before starting an agent session.
24. Run multiple backlog tasks concurrently.
25. Provide customizable dashboards and real-time analytics.
26. Schedule and prioritize tasks with predictive algorithms.
27. Keep team communication inside each workflow.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Product briefs, screen mockups, repository context and backlog items

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect product briefs

4. 3

   Screen mockups

5. 4

   Repository context and backlog items

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, tested, deployed features with fixed-price tickets

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, repository access, skill manifests, context scopes, ticket pricing, approval states, deployment records, billing gates and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: audit the existing codebase and flag what is ready to build; generate specifications and decompose them into user stories with acceptance criteria. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will software product teams and agencies turning requirements and designs into shipped code use it to solve "requirements, specs, tickets, reviews and billing live in separate rented tools, so context is lost between design, build and deployment"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted story points per delivery hour and rework after deployment.
4. Measure, then decide. Track accepted story points per delivery hour and rework after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, tested, deployed features with fixed-price tickets. Retain the explicit scope boundary: One repository, one design source and one deployment target; senior engineer review and client staging approval remain human.

What the build depends on. Repository access and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity delivery requires senior engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository, one design source and one deployment target; senior engineer review and client staging approval remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository and feature set. Offer a monthly delivery allowance after repeat demand. Quote complex integrations or specialist compliance separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, tested, deployed features with fixed-price tickets. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce handoff loss between requirements, code and deployment while keeping senior review and client approval. Demonstrate a concrete reviewed, tested, deployed features with fixed-price tickets using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software product teams and agencies turning requirements and designs into shipped code professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, tested, deployed features with fixed-price tickets from a small authorized input set, with a transparent calculation of accepted story points per delivery hour and rework after deployment and no promised savings.

The first 30 days

1. Week 1: interview five software product teams and agencies turning requirements and designs into shipped code and inspect a recent example of requirements, specs, tickets, reviews and billing living in separate rented tools, so context is lost between design, build and deployment.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted story points per delivery hour and rework after deployment, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted story points per delivery hour and rework after deployment. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted story points per delivery hour and rework after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, tested, deployed features with fixed-price tickets. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved skills, context scopes and review examples, together with reliable delivery for a narrow software niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software product teams and agencies turning requirements and designs into shipped code. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Omniflow, MindyGem, Baseline Core, SonOf and Clears, plus freelancers and generic coding assistants. Compare this product with the buyer's present method on accepted story points per delivery hour and rework after deployment. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model and agent sessions, CI compute, storage, senior reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, tested, deployed features with fixed-price tickets. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.