Source-linked code review and security console

For

Engineering teams and security reviewers shipping code across repositories and pipelines

Solves

Code changes and AI-generated suggestions reach review with bugs, vulnerabilities and quality issues that manual review and scattered scanners miss or bury in noise.

Delivers

Reviewer-approved findings, fixes and tests linked to source lines

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 manual review effort and catch security issues before merge while keeping reviewer control.

1. Review code changes and surface feedback.
2. Detect vulnerabilities and weaknesses in code.
3. Generate and apply fixes for detected issues.
4. Provide real-time suggestions and error detection as code is written.
5. Integrate into code editors and development environments.
6. Run scans inside CI/CD pipelines.
7. Filter low-priority and false-positive alerts.
8. Use surrounding code, history and project patterns for context.
9. Support multiple programming languages and frameworks.
10. Allow teams to configure security rules and coding guidelines.
11. Generate unit tests for changed code.
12. Generate release notes and summaries.
13. Send alerts to team communication tools.
14. Monitor code quality after merge.
15. Integrate with GitHub for repository scanning and interaction.
16. Scan AI-generated code suggestions for security issues.
17. Continuously scan source and dependencies.
18. Adapt recommendations from team conventions and past decisions.
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 reviewer-approved findings, fixes and tests linked to source lines with source references and unresolved questions.

Everything these tools do, in one app

• Code review automation Automatically reviews code changes and provides feedback to reduce manual review effort.Found in Matter AI, Optibot, kluster.ai and 1 more
• Vulnerability detection Identifies security vulnerabilities and weaknesses in code.Found in Matter AI, Corgea, kluster.ai and 4 more
• Automated code fixes Generates and applies fixes for detected issues automatically.Found in Corgea, kluster.ai, Almanax and 1 more
• Real-time feedback Provides immediate suggestions and error detection as code is written or generated.Found in CodeAnt AI, kluster.ai, Snyk Studio and 1 more
• IDE integration Integrates directly into code editors and development environments for seamless workflow.Found in CodeAnt AI, Corgea, kluster.ai and 1 more
• CI/CD pipeline integration Works within continuous integration and deployment pipelines to scan code automatically.Found in Corgea, Almanax
• False positive reduction Filters out low-priority or false-positive alerts to reduce noise.Found in Corgea, Almanax, Checkmarx Next Generation SAST
• Context-aware analysis Uses surrounding code, chat history, or project patterns to tailor suggestions.Found in CodeAnt AI, Optibot, kluster.ai and 1 more
• Multi-language support Supports a wide range of programming languages and frameworks.Found in CodeAnt AI, Corgea, Checkmarx Next Generation SAST and 1 more
• Customizable rules Allows teams to configure security rules or coding guidelines.Found in CodeAnt AI, Matter AI, Optibot and 1 more
• Unit test generation Automatically creates unit tests to improve code reliability.Found in Matter AI
• Release notes generation Generates summaries and detailed release notes for documentation.Found in Matter AI
• Team communication alerts Sends alerts and updates to team communication tools like Slack.Found in Matter AI
• Post-merge monitoring Continues to monitor code quality after changes are merged.Found in Optibot
• GitHub integration Integrates directly with GitHub for repository scanning and interaction.Found in Optibot, VibeSec
• AI-generated code scanning Specifically scans code suggestions generated by AI assistants for security issues.Found in kluster.ai, Snyk Studio, Checkmarx Next Generation SAST
• Continuous scanning Continuously scans source code and dependencies for vulnerabilities.Found in Almanax, VibeSec
• Learning over time Adapts recommendations based on team conventions and past decisions.Found in kluster.ai

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, pull requests, dependency manifests, pipeline events and team rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Pull requests

5. 4

   Dependency manifests

6. 5

   Pipeline events and team rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved findings, fixes and tests linked to source lines

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 repository host and one pipeline provider; final security judgment and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository and rule setup, Review and findings console, Client report and delivery. Use a repository list for projects, a large central diff and findings canvas, and a right-hand panel for rules, context and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome reviewer-approved findings, fixes and tests linked to source lines visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision 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

Author-owned manuscripts, authorized interviews and permitted research sources. Cloud asset storage, design-file import/export and publishing destinations. 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: review code changes and surface feedback; detect vulnerabilities and weaknesses in code. 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 engineering teams and security reviewers shipping code across repositories and pipelines use it to solve "code changes and AI-generated suggestions reach review with bugs, vulnerabilities and quality issues that manual review and scattered scanners miss or bury in noise"?
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 findings per review hour and escaped defects after merge.
4. Measure, then decide. Track accepted findings per review hour and escaped defects 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 host and one pipeline provider; final security judgment and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: review code changes and surface feedback; detect vulnerabilities and weaknesses in code. Support the third module with operator review: generate and apply fixes for detected issues. 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 findings, fixes and tests linked to source lines. Retain the explicit scope boundary: One repository host and one pipeline provider; final security judgment and merge decisions remain human.

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 creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository host and one pipeline provider; final security judgment and merge decisions remain human.

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 asset package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved findings, fixes and tests linked to source lines. 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 review effort and catch security issues before merge while keeping reviewer control. Demonstrate a concrete reviewer-approved findings, fixes and tests linked to source lines using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and security reviewers shipping code across repositories and pipelines 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 findings, fixes and tests linked to source lines from a small authorized input set, with a transparent calculation of accepted findings per review hour and escaped defects after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and security reviewers shipping code across repositories and pipelines and inspect a recent example of code changes and AI-generated suggestions reach review with bugs, vulnerabilities and quality issues that manual review and scattered scanners miss or bury in noise.
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 findings per review hour and escaped defects 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 findings per review hour and escaped defects 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 findings per review hour and escaped defects 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 findings, fixes and tests linked to source lines. 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 rules, repository patterns 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 and security reviewers shipping code across repositories and pipelines. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

CodeAnt AI, Matter AI, Optibot, Corgea, kluster.ai, Almanax, Snyk Studio, Checkmarx Next Generation SAST, aiCode.fail and VibeSec. Compare this product with the buyer's present method on accepted findings per review hour and escaped defects after merge. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image 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 findings, fixes and tests linked to source lines. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. One repository host and one pipeline provider; final security judgment and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.