AI app for it and development · no coding needed
Source-linked code review and security console
Reduce manual review effort and catch security issues before merge while keeping reviewer control.
Made for: Engineering teams and security reviewers shipping code across repositories and pipelines
What it does for you
The problem
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.
What it gives you
Reviewer-approved findings, fixes and tests linked to source lines
What you give it
Repository codepull requestsdependency manifestspipeline eventsteam rules
Build your own version of CodeAnt AI, Matter AI and more
One app with what these 10 AI tools do, yours to keep and change: CodeAnt AI, Matter AI, Optibot, Corgea, kluster.ai, Almanax, Snyk Studio, Checkmarx Next Generation SAST, aiCode.fail, VibeSec.
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
How it works, step by step
- Review code changes and surface feedback
- Detect vulnerabilities and weaknesses in code
- Generate and apply fixes for detected issues
- Provide real-time suggestions and error detection as code is written
- Integrate into code editors and development environments
- Run scans inside CI/CD pipelines
- Filter low-priority and false-positive alerts
- Use surrounding code, history and project patterns for context
- Support multiple programming languages and frameworks
- Allow teams to configure security rules and coding guidelines
- Generate unit tests for changed code
- Generate release notes and summaries
- Send alerts to team communication tools
- Monitor code quality after merge
- Integrate with GitHub for repository scanning and interaction
- Scan AI-generated code suggestions for security issues
- Continuously scan source and dependencies
- Adapt recommendations from team conventions and past decisions
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved findings, fixes and tests linked to source lines with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Source-linked code review and security console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Source-linked code review and security console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce manual review effort and catch security issues before merge while keeping reviewer control. For engineering teams and security reviewers shipping code across repositories and pipelines, convert repository code, pull requests, dependency manifests, pipeline events and team rules into reviewer-approved findings, fixes and tests linked to source lines. The benefit is a testable hypothesis, measured through accepted findings per review hour and escaped defects after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository code, pull requests, dependency manifests, pipeline events and team rules, then follow this sequence: 1. Review code changes and surface feedback. 2. Detect vulnerabilities and weaknesses in code. 3. Generate and apply fixes for detected issues. Resolve uncertain cases with qualified reviewers, approve reviewer-approved findings, fixes and tests linked to source lines, and measure accepted findings per review hour and escaped defects after merge against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
