Source-linked visual interface editing console

For

Front-end developers and product teams maintaining a live web codebase

Solves

Visual edits and code edits live in separate tools, so interface changes drift from the real source and reviews stall.

Delivers

Reviewed, source-linked interface changes

Built in

about 4 weeks of creation time, MVP in 5 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 the gap between visual interface changes and the committed source.

1. Edit interface elements visually and map each change to source.
2. Write edits directly into the codebase, not a separate design file.
3. Show a live preview of the running app during editing.
4. Connect AI agents that generate or apply code edits.
5. Stage, commit and branch from inside the tool.
6. Manage Tailwind CSS classes and styling.
7. Support React, Next.js, Astro and Vite projects.
8. Allow several team members to edit the same project with instant updates.
9. Generate code from visual designs or prompts.
10. Click a preview element to jump to its source.
11. Fork a project into an isolated sandbox copy.
12. Lay out features as cards and dependencies on planning boards.
13. Connect multiple model providers including Claude, OpenAI, Google, xAI and Ollama.
14. Provide a built-in code editor with TypeScript language support.
15. Provide an integrated terminal for running commands.
16. Review code changes automatically for issues.
17. Save checkpoints to revert or compare states.
18. Retain project memory to inform later actions.
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 reviewed, source-linked interface changes with source references and unresolved questions.

Everything these tools do, in one app

• Visual UI editing Lets you change interface elements visually and apply those changes to the code.Found in Shape, Invoke, Tempo Labs and 3 more
• Direct code editing Edits are written directly into the source codebase rather than a separate design file.Found in Shape, Tempo Labs, Inspector and 2 more
• Live preview Shows the running app so you can see changes as you make them.Found in Shape, Invoke, Piny and 2 more
• AI agent integration Connects to AI agents that can generate or apply code edits automatically.Found in Shape, Invoke, Inspector and 1 more
• Git integration Manage version control actions like staging, committing, and branching from within the tool.Found in Shape, Inspector
• Tailwind CSS support Works with Tailwind CSS for styling and class management.Found in Shape, Tempo Labs, Piny and 1 more
• Framework support Supports popular front-end frameworks like React, Next.js, Astro, and Vite.Found in Tempo Labs, Piny, Gemini Canvas and 1 more
• Real-time collaboration Allows multiple team members to work on the same project simultaneously with instant updates.Found in Tempo Labs, Gemini Canvas
• Code generation Generates code from visual designs or prompts.Found in Invoke, Tempo Labs, Gemini Canvas
• Project navigation Click elements in a live preview to jump to their source code.Found in Piny
• Sandbox environment Fork a project into an isolated copy to experiment safely.Found in Invoke
• Visual planning boards Lay out features as cards and dependencies to plan tasks before coding.Found in Invoke
• Multi-model support Connect to multiple AI model providers like Claude, OpenAI, Google, xAI, and Ollama.Found in Invoke
• Built-in code editor Includes a code editor with language support like TypeScript LSP.Found in Invoke
• Integrated terminal Provides a terminal within the tool for running commands.Found in Invoke
• AI code review Automatically reviews code changes for issues.Found in Invoke
• Checkpoints Saves states of the project to revert or compare changes.Found in Invoke
• Memory Remembers context or past interactions to inform future actions.Found in Invoke

What goes in, what comes out

The workflow

1. In
   Start with

   The running app, its source files, styling rules and version history

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect the running app

4. 3

   Its source files

5. 4

   Styling rules and version history

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked interface changes

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 styling setup; final merge and release decisions remain with the engineering owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Project and branch setup, Visual editing canvas with live preview, Source and diff review, Agent and model console, Admin and audit. Use a project list with branch state, a large central preview beside a code panel, and a right-hand panel for element tree, styles, agent actions and comments. Let users compare checkpoints side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant element and source line. Make the task-specific outcome reviewed, source-linked interface changes visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository access, branch and checkpoint state, agent and model settings, reviewer roles, usage allowances, export history 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

Customer-owned repositories, authorized code hosting and permitted design sources. Cloud build and preview environments, design-file import/export and deployment destinations. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized repository access. 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

   5 days

   One buyer segment, one recurring use case; first modules: edit interface elements visually and map each change to source; write edits directly into the codebase, not a separate design file. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   10 days

   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 front-end developers and product teams maintaining a live web codebase use it to solve "visual edits and code edits live in separate tools, so interface changes drift from the real source and reviews stall"?
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 interface changes per developer hour and rework after merge.
4. Measure, then decide. Track accepted interface changes per developer hour and rework after merge; 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 styling setup; final merge and release decisions remain with the engineering owner. Implement one approved repository format, a bounded representative case set and the first two task modules: edit interface elements visually and map each change to source; write edits directly into the codebase, not a separate design file. Support the remaining modules with operator review: show a live preview of the running app during editing; connect AI agents that generate or apply code edits; stage, commit and branch from inside the tool. 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 repository integration. Expand supported frameworks and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, source-linked interface changes. Retain the explicit scope boundary: One supported framework version and one styling setup; final merge and release decisions remain with the engineering owner.

What the build depends on. Repository access 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 supported framework version and one styling setup; final merge and release decisions remain with the engineering owner.

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 package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked interface changes. 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 the gap between visual interface changes and the committed source. Demonstrate a concrete reviewed, source-linked interface changes using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Front-end developers and product teams maintaining a live web codebase professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked interface changes from a small authorized input set, with a transparent calculation of accepted interface changes per developer hour and rework after merge and no promised savings.

The first 30 days

1. Week 1: interview five front-end developers and product teams maintaining a live web codebase and inspect a recent example of visual edits and code edits living in separate tools so interface changes drift from the real source and reviews stall.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted interface changes per developer hour and rework after merge, 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 interface changes per developer hour and rework after merge. 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 interface changes per developer hour and rework after merge; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked interface changes. 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 component patterns, repository constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for front-end developers and product teams maintaining a live web codebase. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Shape, Invoke, Tempo Labs, Inspector, Piny, Gemini Canvas and MightyMeld, plus manual code editing and separate design tools. Compare this product with the buyer's present method on accepted interface changes per developer hour and rework after merge. 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 calls, build and preview compute, 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 reviewed, source-linked interface changes. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering owners approve substantive changes and release scope. One supported framework version and one styling setup; final merge and release decisions remain with the engineering owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.