Local app generation and delivery workspace

For

Developers and technical teams building internal or client apps from plain-language descriptions

Solves

App generation tools run in hosted clouds, scatter project data across subscriptions, and leave no owned, portable codebase.

Delivers

A locally run, versioned, publishable app

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

Generate working apps from plain-language descriptions while keeping projects and data on the user's own machine.

1. Turn a plain-language description into a working app with UI, logic and data.
2. Run generated apps on the user's own machine.
3. Keep project files, prompts and app data on the user's device.
4. Connect the user's own model provider API keys.
5. Show AI session spend in a usage dashboard.
6. Create a restorable version for every change.
7. Store projects as normal folders and git repositories.
8. Provide starter templates for common use cases.
9. Import an existing codebase or repository.
10. Deploy the app through connected deployment services.
11. Detect and install required development tools without terminal access.
12. Refine the app through plain-language iterative edits.
13. Require explicit approval before file or agent access.
14. Output standard Next.js React projects.
15. Keep preferences and context persistent across apps in the workspace.
16. Expose editable UI, logic and data files with visible command logs.
17. Let users choose frameworks and technology stacks.
18. Connect messaging services such as iMessage, Gmail and Slack.

Everything these tools do, in one app

• Natural language app generation Turns a plain-language description into a working app with UI, logic, and data.Found in Capacity Desktop, Moldable, Wandesk and 1 more
• Local app execution Runs the generated apps on the user's own machine rather than in a hosted cloud workspace.Found in Capacity Desktop, Moldable, Wandesk and 1 more
• Local data storage Keeps project files, prompts, and app data on the user's device for privacy and control.Found in Capacity Desktop, Moldable, Wandesk and 1 more
• Bring your own AI keys Lets users connect their own model provider API keys and pay the provider directly.Found in Capacity Desktop, Wandesk
• AI usage spend dashboard Shows what each AI session costs so users can track spending.Found in Capacity Desktop
• Version restore timeline Creates a restorable version for every change so users can roll back a bad AI edit with one click.Found in Capacity Desktop
• Standard project folders Stores projects as normal folders and git repositories that can be opened in any editor or handed to another developer.Found in Capacity Desktop
• Starter template library Provides ready-made templates or starter apps to speed up common use cases.Found in Capacity Desktop, Moldable
• Import existing repository Lets users bring in an existing codebase or repository to continue development.Found in Capacity Desktop, Dualite Alpha
• One-click publishing Connects deployment services and deploys the app directly from the tool.Found in Capacity Desktop, Dualite Alpha
• Automatic dev tool installation Detects and installs required development tools such as Git and Node without terminal access.Found in Capacity Desktop
• Conversational iterative edits Refines the app through plain-language commands like add this or change that.Found in Moldable
• Filesystem permission approvals Requires explicit user approval before the app or agent accesses files.Found in Moldable, Plow Mac App
• Standard web app code output Produces standard Next.js React projects that can be published or adapted outside the tool.Found in Moldable
• Shared memory across apps Keeps preferences and context persistent between different apps in the same workspace.Found in Wandesk
• Editable app structure and change logs Exposes editable files organized as UI, logic, and data with visible logs of AI-run commands.Found in Wandesk
• Framework and stack selection Lets users choose different frameworks and technology stacks to start a project.Found in Dualite Alpha
• Messaging service connectors Connects to services like iMessage, Gmail, and Slack so the agent can read and send messages.Found in Plow Mac App

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language descriptions, existing repositories, chosen stacks and connected service accounts

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language descriptions

4. 3

   Existing repositories

5. 4

   Chosen stacks and connected service accounts

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A locally run, versioned, publishable app

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs 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. Local execution with user-supplied model keys; final code review, security checks and deployment approval remain with the development team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Description and stack setup, Local project workspace, Build and publish. Use a project list with local folder paths, a central editor showing UI, logic and data files, and a right-hand panel for AI chat, permissions and change logs. Let users compare versions and restore from the timeline. Display draft, changes requested and approved states. Provide a spend view per session and a publish panel with deployment targets. Make the task-specific outcome a locally run, versioned, publishable app visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, local folder paths, model key references, permission grants, version history, spend records, deployment targets and a rights record for supplied code. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

User-owned repositories, local development tools and permitted model provider APIs. Cloud deployment services, messaging services such as iMessage, Gmail and Slack, and code hosting. 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.

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: turn a plain-language description into a working app with UI, logic and data; run generated apps on the user's own machine. 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 developers and technical teams building internal or client apps from plain-language descriptions use it to solve "app generation tools run in hosted clouds, scatter project data across subscriptions, and leave no owned, portable codebase"?
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 app changes per developer hour and rollbacks after AI edits.
4. Measure, then decide. Track accepted app changes per developer hour and rollbacks after AI edits; 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 chosen stack and one local operating system; final code review, security checks and deployment approval remain with the development team. Implement one approved input format, a bounded representative case set and the first two task modules: turn a plain-language description into a working app with UI, logic and data; run generated apps on the user's own machine. Support the third module with operator review: keep project files, prompts and app data on the user's device. 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 a locally run, versioned, publishable app. Retain the explicit scope boundary: One chosen stack and one local operating system; final code review, security checks and deployment approval remain with the development team.

What the build depends on. Local file access, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist development QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One chosen stack and one local operating system; final code review, security checks and deployment approval remain with the development team.

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 app package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist stacks separately. These are test prices, not market benchmarks. Package the initial sale as one bounded locally run, versioned, publishable app. 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

Generate working apps from plain-language descriptions while keeping projects and data on the user's own machine. Demonstrate a concrete locally run, versioned, publishable app using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Developers and technical teams building internal or client apps professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample locally run, versioned, publishable app from a small authorized input set, with a transparent calculation of accepted app changes per developer hour and rollbacks after AI edits and no promised savings.

The first 30 days

1. Week 1: interview five developers and technical teams building internal or client apps and inspect a recent example of app generation tools running in hosted clouds, scattering project data across subscriptions and leaving no owned, portable codebase.
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 app changes per developer hour and rollbacks after AI edits, 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 app changes per developer hour and rollbacks after AI edits. 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 app changes per developer hour and rollbacks after AI edits; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a locally run, versioned, publishable app. 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 stacks, local configurations and review examples, together with reliable delivery for a narrow development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for developers and technical teams building internal or client apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Capacity Desktop, Moldable, Wandesk, Dualite Alpha and Plow Mac App. Compare this product with the buyer's present method on accepted app changes per developer hour and rollbacks after AI edits. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, model API usage, 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 a locally run, versioned, publishable app. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Developers approve substantive changes and deployment scope. One chosen stack and one local operating system; final code review, security checks and deployment approval remain with the development team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.