Source-linked UI feedback capture console

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 product teams and developers directing AI coding agents on web interfaces use it to solve "UI feedback for AI coding agents is scattered across screenshots, chat messages and console logs, so agents receive incomplete context and developers repeat clarification cycles"?
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 agent changes per feedback round and clarification messages per resolved annotation.
4. Measure, then decide. Track accepted agent changes per feedback round and clarification messages per resolved annotation; 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 browser family and one preview framework; final code changes and accessibility checks remain developer work. Implement one approved input format, a bounded representative case set and the first two task modules: capture a clicked or pointed element; extract selectors and classes for the exact element. Support the remaining modules with operator review: inspect live CSS, fonts, colors, spacing and layout; annotate several elements and compile the feedback; annotate a specific piece of text; draw an area to capture a region; freeze animations to hold a UI state; surface the React component hierarchy; export the selected component as HTML, CSS or JSX; download SVGs, PNGs and JPGs from the browser; record console errors for the element or page; attach URL, viewport and browser details; accept voice note feedback; accept screenshot feedback; produce structured markdown annotations; connect to AI agents through MCP; sync agent acknowledgements, questions and resolutions; copy the prepared annotation with one keystroke; integrate by dropping a single script into a preview environment; drag prebuilt components to rearrange sections with real-time sync. 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 structured, agent-ready feedback linked to the live page. Retain the explicit scope boundary: One supported browser family and one preview framework; final code changes and accessibility checks remain developer work.

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 browser family and one preview framework; final code changes and accessibility checks remain developer work.

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined capture package. Offer a monthly production allowance after repeat demand. Quote complex multi-framework or enterprise integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded structured, agent-ready feedback linked to the live page. 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 clarification cycles between reviewers and AI coding agents while preserving the reviewer's intent. Demonstrate a concrete structured, agent-ready feedback linked to the live page using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product teams and developers directing AI coding agents on web interfaces professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample structured, agent-ready feedback linked to the live page from a small authorized input set, with a transparent calculation of accepted agent changes per feedback round and clarification messages per resolved annotation and no promised savings.

The first 30 days

1. Week 1: interview five product teams and developers directing AI coding agents on web interfaces and inspect a recent example of UI feedback for AI coding agents is scattered across screenshots, chat messages and console logs, so agents receive incomplete context and developers repeat clarification cycles.
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 agent changes per feedback round and clarification messages per resolved annotation, 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 agent changes per feedback round and clarification messages per resolved annotation. 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 agent changes per feedback round and clarification messages per resolved annotation; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs structured, agent-ready feedback linked to the live page. 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 annotation patterns, preview 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 product teams and developers directing AI coding agents on web interfaces. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Agentation, Qursor, Patchrooms, manual screenshots and chat messages. Compare this product with the buyer's present method on accepted agent changes per feedback round and clarification messages per resolved annotation. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Capture processing, 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 structured, agent-ready feedback linked to the live page. Track cost per accepted output, including correction work, unsuccessful cases and support.