Source-linked autonomous code repair console

For

Engineering teams maintaining multi-repository codebases

Solves

Bugs, style drift and design debt accumulate faster than developers can find, fix and verify them across repositories.

Delivers

Reviewer-approved code changes linked to their source evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce manual repair cycles while keeping every change reviewable and attributable.

1. Detect bugs and failing tests across repositories.
2. Propose and apply coordinated multi-file fixes.
3. Maintain cross-file context to reduce regressions.
4. Run changes through a command-line interface in existing pipelines.
5. Support team-level coordination and review workflows.
6. Polish and update code documentation.
7. Enforce code style and quality standards.
8. Optimize individual functions for performance or readability.
9. Suggest and apply system-design refinements.
10. Browse, edit and execute files through an agent-computer interface.
11. Adapt to tasks beyond debugging, such as security checks and competitive coding.
12. Learn individual technical and workflow preferences over time.
13. Trace linked conversations and context forks without rebuilding context.
14. Test changes in an isolated sandbox before merging.
15. Log failed approaches as negative constraints for future sessions.
16. Report bugs fixed and features shipped on an autonomy dashboard with grading targets.
17. Run background agents for repetitive granular tasks.
18. Hold entire repositories or long technical documents in context without truncation.
19. Distribute under an open-source license for modification and commercial use.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewer-approved code changes linked to their source evidence with source references and unresolved questions.

Everything these tools do, in one app

• Autonomous bug fixing Automatically identifies and fixes bugs in code without human intervention.Found in Polarity, SWE-agent, BobCA and 1 more
• Codebase-wide changes Makes coordinated changes across multiple files rather than isolated single-file edits.Found in Polarity, SWE-agent, Hy4 preview
• Context engine Maintains cross-file consistency and reduces regressions by understanding the entire codebase.Found in Polarity, BobCA, Hy4 preview
• CLI integration Integrates into existing developer pipelines via command-line interface.Found in Polarity
• Team coordination Supports team-level coordination and workflows.Found in Polarity
• Documentation polishing Automatically improves and polishes code documentation.Found in Polarity
• Style enforcement Enforces code style and quality standards automatically.Found in Polarity
• Function optimization Optimizes individual functions for better performance or readability.Found in Polarity
• System-design refinements Suggests and applies improvements to system design.Found in Polarity
• Agent-Computer Interface Enables AI to browse, edit, and execute files in a repository through a specialized interface.Found in SWE-agent
• Versatility to other tasks Adapts to tasks beyond debugging, such as cybersecurity challenges and competitive coding.Found in SWE-agent
• User preference learning Learns and adapts to individual technical and workflow preferences over time.Found in BobCA
• Linked conversations Visual tracing of idea branches and context forks to carry forward or fork logic without rebuilding context.Found in BobCA
• Sandbox testing Provides an isolated environment to test changes before merging.Found in BobCA
• Negative constraint logging Records failed approaches to avoid repeating them in future sessions.Found in BobCA
• Autonomy HUD Real-time dashboard reporting bugs fixed and features shipped, with tools to set grading targets and monitor agent activity.Found in BobCA
• Background agents Specialized agents that handle repetitive, granular tasks in the background.Found in BobCA
• Large context window Accommodates entire code repositories or long technical documents without truncation.Found in Hy4 preview
• Open-source distribution Allows modification and commercial use under an open-source license.Found in Hy4 preview

What goes in, what comes out

The workflow

1. In
   Start with

   Repository access, issue trackers, test suites and team conventions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository access

4. 3

   Issue trackers

5. 4

   Test suites and team conventions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved code changes linked to their source evidence

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 repository set and approved toolchain; final merge and security 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 agent setup, Change review console, Autonomy and value dashboard. Use a repository list with agent status, a central diff and evidence canvas, and a right-hand panel for linked conversations, constraints and approvals. Let users compare branches and agent runs side by side. Display proposed, tested, changes requested and merged states. Provide a reviewer link with comments anchored to the relevant file and line. Make the task-specific outcome reviewer-approved code changes linked to their source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository versions, reviewer comments, approval states, usage allowances, agent run limits, merge 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

Customer-owned repositories, authorized issue trackers and permitted test suites. Cloud code storage, CI/CD import/export and deployment destinations. Start with file exchange and validate destination specifications before promising direct deployment. 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: detect bugs and failing tests across repositories; propose and apply coordinated multi-file fixes. 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 multi-repository codebases use it to solve "bugs, style drift and design debt accumulate faster than developers can find, fix and verify them across repositories"?
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 changes per engineering hour and regressions after merge.
4. Measure, then decide. Track accepted changes per engineering hour and regressions 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 repository set and approved toolchain; final merge and security decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: detect bugs and failing tests across repositories; propose and apply coordinated multi-file fixes. Support the remaining modules with operator review: maintain cross-file context to reduce regressions; run changes through a command-line interface in existing pipelines. 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 reviewer-approved code changes linked to their source evidence. Retain the explicit scope boundary: One repository set and approved toolchain; final merge and security decisions remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous agent 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 set and approved toolchain; final merge and security 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 security, migration or specialist engineering separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved code changes linked to their source evidence. 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 repair cycles while keeping every change reviewable and attributable. Demonstrate a concrete reviewer-approved code changes linked to their source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams maintaining multi-repository codebases professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved code changes linked to their source evidence from a small authorized input set, with a transparent calculation of accepted changes per engineering hour and regressions after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams maintaining multi-repository codebases and inspect a recent example of bugs, style drift and design debt accumulating faster than developers can find, fix and verify them across repositories.
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 changes per engineering hour and regressions 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 changes per engineering hour and regressions 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 changes per engineering hour and regressions 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 reviewer-approved code changes linked to their source evidence. 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 repair 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 multi-repository codebases. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Polarity, SWE-agent, BobCA and Hy4 preview, plus manual code review and internal scripts. Compare this product with the buyer's present method on accepted changes per engineering hour and regressions after merge. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Agent run attempts, repository 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 reviewer-approved code changes linked to their source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering teams approve substantive changes and deployment scope. One repository set and approved toolchain; final merge and security 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.