Source-linked test generation and maintenance console

For

Engineering teams maintaining application test suites alongside frequent code changes

Solves

Code changes outpace test coverage, and broken or outdated tests consume developer time instead of catching real defects.

Delivers

Reviewed, committed test code and triage decisions linked to their sources

Built in

about 5 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual test upkeep while keeping tests in the team's own repository.

1. Generate tests from natural language prompts, code changes or user feedback.
2. Scan unstaged changes or branch diffs to identify what to test.
3. Create AI-generated test plans outlining scenarios to run.
4. Target auth boundaries and edge flows, not just easy passing cases.
5. Run generated tests to verify application behavior.
6. Execute tests in a live browser for end-to-end validation.
7. Analyze failures to diagnose real bugs versus broken tests.
8. Auto-fix broken tests to maintain suite health.
9. Keep tests up to date as the application changes.
10. Generate and run tests automatically on every pull request.
11. Route real bugs to issue tracking or communication tools.
12. Commit tests as standard Playwright code in the team's own repository.
13. Generate evaluation metrics for AI models and applications.
14. Score outputs across quality dimensions with fast scoring models.
15. Calibrate scoring with human feedback, labeled data or preference pairs.
16. Export evaluation logic as code and support reward modeling and agent control flow.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed test and triage record with source references and unresolved questions.

Everything these tools do, in one app

• Automated test generation Automatically creates tests based on natural language prompts, code changes, or user feedback.Found in Octomind MCP, Checksum AI, Expect and 1 more
• Test execution Runs the generated tests to verify application behavior.Found in Octomind MCP, Checksum AI, Expect
• Failure analysis and triage Analyzes test failures to diagnose issues and determine whether they are real bugs or broken tests.Found in Octomind MCP, Checksum AI
• Auto-fixing of test failures Automatically fixes broken tests to maintain test suite health.Found in Octomind MCP, Checksum AI
• Continuous test maintenance Keeps tests up-to-date as the application changes, reducing manual upkeep.Found in Octomind MCP, Checksum AI
• CI/CD integration Integrates with CI/CD pipelines to automate testing within development workflows.Found in Octomind MCP
• Natural language input Allows users to create tests using natural language prompts.Found in Octomind MCP
• Pull request testing Generates and runs tests automatically on every pull request.Found in Checksum AI
• Bug routing Sends real bugs to issue tracking or communication tools like Jira, Linear, or Slack.Found in Checksum AI
• Standard Playwright code Tests are committed as standard Playwright code in the team's own repository, avoiding lock-in.Found in Checksum AI
• Edge case targeting Focuses on testing auth boundaries and edge flows, not just easy passing cases.Found in Checksum AI
• Diff-based testing Scans unstaged changes or branch diffs to identify what to test.Found in Expect
• AI-generated test plans Creates test plans that outline scenarios to run against the application.Found in Expect
• Live browser execution Runs tests in a live browser for realistic end-to-end validation.Found in Expect
• Agent-driven workflow Integrates into developer or QA processes for faster feedback.Found in Expect
• Evaluation metric generation Automatically generates evaluation metrics for AI models and applications.Found in Pi Copilot
• Fast scoring models Uses proprietary models to provide fast and consistent scoring across many quality dimensions.Found in Pi Copilot
• Calibration with human feedback Supports calibration using human feedback, labeled data, or preference pairs.Found in Pi Copilot
• Integration with data and AI tools Integrates with tools like Sheets, PromptFoo, and GRPO.Found in Pi Copilot
• Export evaluation logic Allows exporting evaluation logic as code.Found in Pi Copilot
• Reward modeling and agent control Models can be used for reward modeling in reinforcement learning and agent control flow.Found in Pi Copilot

What goes in, what comes out

The workflow

1. In
   Start with

   Repository access, branch diffs, natural language prompts, CI results and reviewer feedback

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository access

4. 3

   Branch diffs

5. 4

   Natural language prompts

6. 5

   CI results and reviewer feedback

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed, committed test code and triage decisions linked to their sources

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. Final test approval, bug triage and merge decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository and prompt intake, Editable test and triage preview, CI and delivery status. Use a thumbnail gallery for repositories and runs, a large central editing canvas for generated test code and failure analysis, and a right-hand panel for sources, constraints and comments. Let users compare generated tests against existing suites side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant test or failure. Make the task-specific outcome reviewed, committed test code and triage decisions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, reviewer 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

Team-owned repositories, CI/CD pipelines, issue tracking and communication tools such as Jira, Linear or Slack, and data and AI tools such as Sheets, PromptFoo and GRPO. 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

   5 days

   One buyer segment, one recurring use case; first modules: generate tests from natural language prompts, code changes or user feedback; scan unstaged changes or branch diffs to identify what to test. 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

   2 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 engineering teams maintaining application test suites alongside frequent code changes use it to solve "code changes outpace test coverage, and broken or outdated tests consume developer time instead of catching real defects"?
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 tests per developer hour and test-suite health after changes.
4. Measure, then decide. Track accepted tests per developer hour and test-suite health after changes; 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 repository and one CI provider; final test approval, bug triage and merge decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: generate tests from natural language prompts, code changes or user feedback; scan unstaged changes or branch diffs to identify what to test. Support the remaining modules with operator review: create AI-generated test plans; target auth boundaries and edge flows; run generated tests; execute in a live browser; analyze failures; auto-fix broken tests; keep tests up to date; generate and run tests on every pull request; route real bugs; commit tests as standard Playwright code; generate evaluation metrics; score outputs; calibrate scoring; export evaluation logic; support reward modeling. 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 reviewed, committed test code and triage decisions. Retain the explicit scope boundary: One repository and one CI provider; final test approval, bug triage and merge decisions remain with the engineering team.

What the build depends on. Repository access and preview, asynchronous generation 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 repository and one CI provider; final test approval, bug triage and merge decisions remain with the engineering 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 repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or specialist evaluation work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed test and triage record. 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 manual test upkeep while keeping tests in the team's own repository. Demonstrate a concrete reviewed test and triage record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams maintaining application test suites alongside frequent code changes professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample test and triage record from a small authorized input set, with a transparent calculation of accepted tests per developer hour and test-suite health after changes and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams maintaining application test suites alongside frequent code changes and inspect a recent example of code changes outpacing test coverage and broken or outdated tests consuming developer time.
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 tests per developer hour and test-suite health after changes, 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 tests per developer hour and test-suite health after changes. 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 tests per developer hour and test-suite health after changes; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, committed test code and triage decisions. 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, 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 engineering teams maintaining application test suites alongside frequent code changes. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Octomind MCP, Checksum AI, Expect and Pi Copilot, plus manual test writing and generic CI scripts. Compare this product with the buyer's present method on accepted tests per developer hour and test-suite health after changes. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, browser execution, 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 test and triage records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, test accuracy and usage permissions. Engineering teams approve substantive changes and merge scope. One repository and one CI provider; final test approval, bug triage and merge decisions remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.