Chat-built full-stack app delivery workspace

For

Small product teams and internal builders shipping full-stack web apps

Solves

Building, hosting and iterating a full-stack app requires stitching together separate coding, database, hosting and deployment tools.

Delivers

A running, source-accessible full-stack app with a live link

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

Reduce tool sprawl while keeping the generated app and its source under the team's control.

1. Create and modify apps through chat.
2. Generate frontend, backend, database, authentication and payments code.
3. Store app data in a built-in database.
4. Deploy and run the app on built-in hosting.
5. Show a live preview while editing.
6. Publish to a live link in one step.
7. Expose the generated source code.
8. Keep a persistent versioned workspace with files, diffs, checkpoints and logs.
9. Build and run the app in the same environment.
10. Provide a private dedicated VM with the coding agent preinstalled.
11. Produce Next.js projects following routing and state conventions.
12. Reproduce high-fidelity interfaces and synchronize media where supported.
13. Include authentication and payment flows without third-party integrations.
14. Apply certain bug fixes automatically during iteration.
15. Host in the cloud with logging, key management and data storage.
16. Wrap code into live multi-user API endpoints.
17. Provide asynchronous compute and embedding search.
18. Offer pre-integrated AI components such as model, memory, search and transcription.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned running, source-accessible full-stack app with a live link with source references and unresolved questions.

Everything these tools do, in one app

• Chat-driven app building Lets users create and modify apps by talking to an AI assistant in chat.Found in AppWizzy, JDoodle.ai MCP, Orchids
• Full-stack code generation Generates frontend, backend, database, authentication, and payments code for an app.Found in Orchids
• Built-in database Provides persistent storage for app data without adding a separate database service.Found in JDoodle.ai MCP, Orchids, AppWizzy
• Built-in hosting Deploys and runs the app so it can be used online.Found in JDoodle.ai MCP, Orchids, AppWizzy and 1 more
• Live preview Shows changes to the app in real time while editing.Found in JDoodle.ai MCP
• One-step publishing Publishes the project to a live link in a single step.Found in JDoodle.ai MCP
• Source code access Gives users direct access to the generated source code.Found in AppWizzy
• Persistent versioned workspace Keeps files, diffs, Git/checkpoints, logs, and build state for recovery and iteration tracking.Found in AppWizzy
• In-environment build and run Builds and runs the app in the same environment where it was created instead of exporting it elsewhere.Found in AppWizzy
• Private dedicated VM Provides a private virtual machine with an AI coding agent preinstalled.Found in AppWizzy
• Next.js project output Produces projects that follow Next.js conventions for routing and state handling.Found in Orchids
• High-fidelity UI reproduction Generates accurate interfaces and can synchronize media in many cases.Found in Orchids
• Built-in authentication and payments Includes auth and payment flows in generated apps without third-party integrations.Found in Orchids
• Free automatic bug fixes Applies certain fixes without consuming credits during iteration.Found in JDoodle.ai MCP
• Managed cloud hosting Hosts applications in the cloud with logging, key management, and data storage.Found in Steamship
• Flask-style endpoints Wraps code with minimal changes into live, multi-user APIs.Found in Steamship
• Async compute and embedding search Provides asynchronous compute and embedding search for AI applications.Found in Steamship
• Pre-integrated AI swap-ins Offers out-of-the-box support for components such as OpenAI, memory variants, Google Search, and audio transcription.Found in Steamship

What goes in, what comes out

The workflow

1. In
   Start with

   Chat instructions, project conventions and deployment constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect chat instructions

4. 3

   Project conventions and deployment constraints

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A running, source-accessible full-stack app with a live link

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 supported framework version and one deployment target; security review and production release remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Chat build workspace, Editable code and preview, Deployment and operations. Use a project list with environment status, a central chat and editor canvas, and a right-hand panel for files, diffs, logs and settings. Let users compare versions side by side. Display draft, building, running and published states. Provide a client preview link with comments anchored to the relevant screen. Make the task-specific outcome a running, source-accessible full-stack app with a live link visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, environment versions, client comments, approval states, usage allowances, revision 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, authorized design files and permitted data sources. Cloud hosting, source control, CI and deployment 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.

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: create and modify apps through chat; generate frontend, backend, database, authentication and payments code. 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 small product teams and internal builders shipping full-stack web apps use it to solve "building, hosting and iterating a full-stack app requires stitching together separate coding, database, hosting and deployment tools"?
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 delivery hour and rework after publishing.
4. Measure, then decide. Track accepted app changes per delivery hour and rework after publishing; 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 supported framework version and one deployment target; security review and production release remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create and modify apps through chat; generate frontend, backend, database, authentication and payments code. Support the third module with operator review: store app data in a built-in database. 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 running, source-accessible full-stack app with a live link. Retain the explicit scope boundary: One supported framework version and one deployment target; security review and production release remain human.

What the build depends on. Asset upload and preview, 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 supported framework version and one deployment target; security review and production release 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 app package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist security work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded running, source-accessible full-stack app with a live link. 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 tool sprawl while keeping the generated app and its source under the team's control. Demonstrate a concrete running, source-accessible full-stack app with a live link using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Small product teams and internal builders shipping full-stack web apps professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample running, source-accessible full-stack app with a live link from a small authorized input set, with a transparent calculation of accepted app changes per delivery hour and rework after publishing and no promised savings.

The first 30 days

1. Week 1: interview five small product teams and internal builders shipping full-stack web apps and inspect a recent example of building, hosting and iterating a full-stack app requires stitching together separate coding, database, hosting and deployment tools.
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 delivery hour and rework after publishing, 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 delivery hour and rework after publishing. 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 delivery hour and rework after publishing; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a running, source-accessible full-stack app with a live link. 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 project templates, deployment constraints 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 small product teams and internal builders shipping full-stack web apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AppWizzy, JDoodle.ai MCP, Orchids, Steamship, freelancers, agencies and separate coding, database and hosting subscriptions. Compare this product with the buyer's present method on accepted app changes per delivery hour and rework after publishing. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, compute and hosting, 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 running, source-accessible full-stack app with a live link. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source ownership, dependency licenses, secret handling and deployment permissions. Named owners approve production releases and external actions. One supported framework version and one deployment target; security review and production release remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.