Source-linked autonomous web app bug testing console

For

Engineering teams shipping web applications who need bugs found and reported before release

Solves

Manual and scripted testing misses bugs, and findings arrive without root cause, fix or reproducible session context.

Delivers

Developer-reviewed bug reports with root cause, fix suggestions and replayable sessions

Built in

about 4 weeks of creation time, MVP in 4 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 debugging time while keeping developers in control of every fix.

1. Explore the app autonomously without pre-written scripts.
2. Interact with elements and detect bugs.
3. Post findings and fixes on GitHub pull requests.
4. Identify the likely cause of each bug.
5. Generate a code change suggestion to fix the bug.
6. Pass root cause and fix to coding agents.
7. Record every session with complete browser state.
8. Capture console logs and network requests.
9. Capture DOM state for debugging without re-running the failure.
10. Handle authentication via credentials, auto-account creation or skip.
11. Support time-travel debugging in familiar DevTools.
12. Run real code in a secure sandbox.
13. Clone repositories automatically for contextual analysis.
14. Monitor continuously through the code review process.
15. Convert plain language into automated test cases.
16. Adapt tests automatically when UI elements change.
17. Run test suites on a schedule without supervision.
18. Allow manual intervention or adjustment of tests.

Everything these tools do, in one app

• Autonomous app exploration The tool crawls the app and interacts with elements without pre-written scripts to find bugs.Found in Replay QA, AI QA
• Bug detection Automatically identifies bugs in the application.Found in Replay QA, Jazzberry, AI QA
• GitHub integration Integrates with GitHub to post findings and fixes on pull requests.Found in Replay QA, Jazzberry
• Root cause analysis Identifies the likely cause of each bug found.Found in Replay QA
• Fix suggestions Generates a code change suggestion to fix the bug.Found in Replay QA
• Coding agent integration Passes root cause and fix to coding agents like Cursor or Claude Code.Found in Replay QA
• Session recording Records every session with complete browser state for debugging.Found in Replay QA
• Console and network logs Captures console logs and network requests for debugging.Found in Replay QA
• DOM state capture Captures DOM state to enable debugging without re-running the failure.Found in Replay QA
• Auth handling Allows providing credentials, auto-account creation, or skipping authentication.Found in Replay QA
• Time-travel debugging Enables debugging in familiar DevTools with full session context.Found in Replay QA
• Sandboxed code execution Runs real code in a secure sandbox to identify bugs realistically.Found in Jazzberry
• Automatic repository cloning Clones repositories automatically to analyze code changes contextually.Found in Jazzberry
• Continuous monitoring Provides ongoing feedback throughout the code review process.Found in Jazzberry
• Natural language test creation Converts plain language inputs into automated test cases.Found in AI QA
• Self-healing tests Adapts tests automatically when UI elements change.Found in AI QA
• Scheduled test execution Runs automated test suites on a schedule without supervision.Found in AI QA
• Manual test control Allows manual intervention or adjustment of tests as needed.Found in AI QA

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized app URL, test credentials and repository access

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect an authorized app URL

4. 3

   Test credentials and repository access

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Developer-reviewed bug reports with root cause, fix suggestions and replayable sessions

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. One authorized target app and repository per pilot; final triage and code changes remain developer decisions. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Target and access setup, Live exploration and findings, Report and handoff. Use a run list for targets, a large central findings view with session replay, and a right-hand panel for root cause, fix suggestion and logs. Let users compare runs side by side. Display open, triaged, fixed and dismissed states. Provide a developer handoff link with findings anchored to the relevant pull request. Make the task-specific outcome developer-reviewed bug reports with root cause, fix suggestions and replayable sessions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, target versions, developer comments, approval states, usage allowances, run 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

Developer-owned repositories, CI pipelines and issue trackers. Cloud code hosting, pull-request APIs and notification destinations. Start with file exchange and validate destination specifications before promising direct posting. 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

   4 days

   One buyer segment, one recurring use case; first modules: explore the app autonomously without pre-written scripts; interact with elements and detect bugs. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   9 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 engineering teams shipping web applications who need bugs found and reported before release use it to solve "manual and scripted testing misses bugs, and findings arrive without root cause, fix or reproducible session context"?
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: Confirmed bugs per testing hour and time from finding to merged fix.
4. Measure, then decide. Track confirmed bugs per testing hour and time from finding to merged fix; 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 authorized target app and repository per pilot; final triage and code changes remain developer decisions. Implement one approved input format, a bounded representative case set and the first two task modules: explore the app autonomously without pre-written scripts; interact with elements and detect bugs. Support the third module with operator review: post findings and fixes on GitHub pull requests. 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 developer-reviewed bug reports with root cause, fix suggestions and replayable sessions. Retain the explicit scope boundary: One authorized target app and repository per pilot; final triage and code changes remain developer decisions.

What the build depends on. App access and preview, asynchronous exploration 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 authorized target app and repository per pilot; final triage and code changes remain developer decisions.

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 target app and repository. Offer a monthly testing allowance after repeat demand. Quote complex multi-app or regulated environments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded developer-reviewed bug reports with root cause, fix suggestions and replayable sessions. 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 debugging time while keeping developers in control of every fix. Demonstrate a concrete developer-reviewed bug reports with root cause, fix suggestions and replayable sessions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams shipping web applications professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample developer-reviewed bug reports with root cause, fix suggestions and replayable sessions from a small authorized input set, with a transparent calculation of confirmed bugs per testing hour and time from finding to merged fix and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams shipping web applications and inspect a recent example of manual and scripted testing misses bugs, and findings arrive without root cause, fix or reproducible session context.
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 confirmed bugs per testing hour and time from finding to merged fix, 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: Confirmed bugs per testing hour and time from finding to merged fix. 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

Confirmed bugs per testing hour and time from finding to merged fix; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs developer-reviewed bug reports with root cause, fix suggestions and replayable sessions. 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 test patterns, app 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 engineering teams shipping web applications. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Replay QA, Jazzberry and AI QA, plus manual QA and scripted test suites. Compare this product with the buyer's present method on confirmed bugs per testing hour and time from finding to merged fix. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Exploration runs, sandbox compute, storage, reviewer hours, developer revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of developer-reviewed bug reports with root cause, fix suggestions and replayable sessions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve developer intent, source attribution, code accuracy and usage permissions. Developers approve substantive changes and deployment scope. One authorized target app and repository per pilot; final triage and code changes remain developer decisions. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.