
Prompt-to-app delivery workspace
Reduce manual coding and handoffs while keeping the team in control of the shipped app.
- For
- Product teams and agencies turning app ideas or designs into working web and mobile applications
- Solves
- App ideas and Figma designs stall between prototype and a deployable product because generation, editing, backend wiring, review and release sit in separate tools.
- Delivers
- Reviewed, exportable and deployable application
- Built in
- about 6 weeks of creation time, MVP in 7 days
- Investment
- $13,500 for the MVP, $46,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
Reduce manual coding and handoffs while keeping the team in control of the shipped app.
- Generate a working application from a description or prompt.
- Convert Figma designs into app code.
- Adjust UI, layout and components in a visual editor.
- Build interfaces by dragging and dropping elements.
- Connect apps to databases, APIs and backend services.
- Export the generated source code.
- Deploy the app with a single action.
- Support multiple people working on the same app.
- Connect external services and APIs.
- Start projects from pre-built templates.
- Clarify requirements and plan the product before building.
- Arrange multiple screen designs on a canvas.
- Monitor app performance with live reports.
- Set up triggers and actions to automate tasks.
- Identify and fix errors in the development workflow.
- Optimize the app for search engines.
- Manage permissions, audit logs and SSO for teams.
- Ship apps to app stores and the web.
Everything these tools do, in one app
- AI app generation Generates a working application from a description or prompt.Found in Bricabrac AI, Polygram, Dreamflow 2.0 and 2 more
- Figma-to-code conversion Turns Figma designs into code for apps.Found in App2.dev, Bravo Studio
- Visual editor Lets users adjust UI, layout, and components visually.Found in Bravo Studio, Bricabrac AI, Raydian and 1 more
- Drag-and-drop builder Builds app interfaces by dragging and dropping elements.Found in Bravo Studio, Bricabrac AI, AppWeaver and 1 more
- Backend integration Connects apps to databases, APIs, and backend services.Found in App2.dev, Bravo Studio, Raydian and 2 more
- Code export Allows exporting the generated source code.Found in Bricabrac AI, Raydian, Dreamflow 2.0 and 1 more
- One-click deployment Deploys the app with a single action.Found in App2.dev, AppWeaver, UI Bakery App Agent and 1 more
- Collaboration tools Supports multiple people working together on the same app.Found in Polygram, AppWeaver, UI Bakery App Agent
- Third-party integrations Connects to external services and APIs.Found in AppWeaver, DreamFlow, Raydian and 1 more
- Pre-built templates Provides ready-made templates to start projects faster.Found in AppWeaver
- AI product planning Helps clarify requirements and plan the product before building.Found in Polygram
- Multi-screen design canvas Generates and arranges multiple screen designs on a canvas.Found in Polygram
- Real-time analytics Monitors workflow or app performance with live reports.Found in DreamFlow
- Customizable triggers Sets up triggers and actions to automate tasks.Found in DreamFlow
- Visual debugger Helps identify and fix errors within the development workflow.Found in EasyCode
- SEO optimization Optimizes the app for search engines.Found in EasyCode
- Access controls Manages permissions, audit logs, and SSO for teams.Found in UI Bakery App Agent
- Publish to app stores Supports shipping apps to app stores and web.Found in Dreamflow 2.0
What goes in, what comes out
- App description
- Figma file
- Data model
- Release constraints
AI drafts, people review. Technical delivery workspace with managed implementation.
- Reviewed
- Exportable
- Deployable application
How it works
The workflow
- InStart with
App description, Figma file, data model and release constraints
- 1
Confirm the buyer's problem and scope
- 2
Collect the app description
- 3
Figma file
- 4
Data model and release constraints
- 5
Then follow this sequence: 1
- OutFinish with
Reviewed, exportable and deployable application
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. One target platform and one approved backend pattern; security review and release approval remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Project brief and sources, Build workspace, Review and release. Use a project gallery, a central multi-screen canvas with a visual editor, and a right-hand panel for data connections, triggers, permissions and comments. Let users compare generated versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant screen. Make the task-specific outcome reviewed, exportable and deployable application visible beside its evidence, review state and value baseline.
Accounts and administration
Project ownership, source versions, client comments, approval states, environment allowances, build limits, download history 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
Team-owned repositories, design files and data sources. Cloud build and hosting, design-file import/export, app store and web publishing 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.
How we build it
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
Scoping call
Day 1Thirty 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
MVP
7 daysOne buyer segment, one recurring use case; first modules: generate a working application from a description or prompt; convert Figma designs into app code. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
8 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
3 weeksSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will product teams and agencies turning app ideas or designs into working web and mobile applications use it to solve "app ideas and Figma designs stall between prototype and a deployable product because generation, editing, backend wiring, review and release sit in separate tools"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted screens per delivery hour and post-release defect rate.
- Measure, then decide. Track accepted screens per delivery hour and post-release defect rate; 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 target platform and one approved backend pattern; security review and release approval remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate a working application from a description or prompt; convert Figma designs into app code. Support the remaining modules with operator review: visual editing, backend connection, code export and one-click 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 the reviewed, exportable and deployable application. Retain the explicit scope boundary: One target platform and one approved backend pattern; security review and release approval remain human.
What the build depends on. Source upload and preview, asynchronous build jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One target platform and one approved backend pattern; security review and release approval remain human.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: generate a working application from a description or prompt; convert Figma designs into app code. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$46,000about 6 weeks of creation time · start with the MVP from $13,500
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $60–$120 | $90–$180 |
| Full productabout 50 customers | $110–$210 | $530–$1,050 | $640–$1,260 |
Run it or resell it
For your own team
Product teams and agencies turning app ideas or designs into working web and mobile applications run it inside the business: app description, Figma file, data model and release constraints in, reviewed, exportable and deployable application out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#277c91 - accent
#c97f54 - surface
#e4eef1 - ink
#22201e
- Headings
- Fraunces
- Text
- Inter
- Voice
- Technical, direct, no hype
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 app package. Offer a monthly production allowance after repeat demand. Quote complex native, offline or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, exportable and deployable application. 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 manual coding and handoffs while keeping the team in control of the shipped app. Demonstrate a concrete reviewed, exportable and deployable application using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Product teams and agencies turning app ideas or designs into working web and mobile applications professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample reviewed, exportable and deployable application from a small authorized input set, with a transparent calculation of accepted screens per delivery hour and post-release defect rate and no promised savings.
The first 30 days
- Week 1: interview five product teams and agencies turning app ideas or designs into working web and mobile applications and inspect a recent example of app ideas and Figma designs stall between prototype and a deployable product because generation, editing, backend wiring, review and release sit in separate tools.
- Week 2: prepare a consented or synthetic demonstration of the stated task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure accepted screens per delivery hour and post-release defect rate, 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 screens per delivery hour and post-release defect rate. 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 screens per delivery hour and post-release defect rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs a reviewed, exportable and deployable application. 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 components, backend patterns and review examples, together with reliable delivery for a narrow app niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product teams and agencies turning app ideas or designs into working web and mobile applications. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
App2.dev, Bravo Studio, Bricabrac AI, Polygram, AppWeaver, Raydian, DreamFlow, Dreamflow 2.0, UI Bakery App Agent and EasyCode. Compare this product with the buyer's present method on accepted screens per delivery hour and post-release defect rate. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Generation attempts, build and test runs, storage, 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 the reviewed, exportable and deployable application. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Preserve source attribution, license terms and security requirements. Named owners approve substantive changes and release scope. One target platform and one approved backend pattern; security review and release approval remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.