Audit Bench Ai combines LLM reasoning with static analysis to catch the security holes, logic bugs, and framework misuse that traditional linters miss — on a single file, a pull request, or a whole repository.
Doing technical due diligence on a software acquisition? Get a risk report in days, not weeks →
Every provider runs the same three-stage pipeline and the same review lenses — pick the one that fits your budget or your existing contract, per audit or as your account default.
Modern AI coding assistants generate code quickly but frequently introduce hidden bugs, security issues, and framework misuse. Traditional linters catch syntax problems — not business logic or intent.
Reviews. Gates. Fixes. Reports. Same findings whether it's a file, a PR, or a whole repository.
Built for how teams actually ship
Beyond one-off audits: review the code your team merges, track quality trends over time, and wire it into the pipeline you already have.
Pull & merge request review
Review a GitHub PR or GitLab MR scoped to just the changed lines — findings post back as inline review comments, a summary, and a merge-blocking status check.
Team analytics dashboard
Security, performance, and technical-debt scores trended over time, plus a breakdown of your most common findings and riskiest files.
CLI for CI pipelines
Run `auditbench scan` or `auditbench audit` from a pipeline step, a pre-commit hook, or a terminal — same engine, same findings, scriptable output.
Role-ready plans & quotas
Daily and monthly AI-audit limits per plan, usage-based — not a flat seat count — so cost scales with what a team actually reviews.
Lives inside the review you're already doing
Not another tab to check. Findings, gates, and answers show up directly on the PR or MR.
Inline PR & MR comments
Findings land as real review comments on the exact changed lines — GitHub review threads or GitLab discussions — not a dashboard you have to remember to check.
Auto-generated summary
Every review posts a plain-English walkthrough alongside the inline comments — verdict, finding counts, and a link to the full report.
Merge-blocking quality gates
A commit status check reports pass/fail on every PR and MR. Wire it into branch protection and stop shippable-looking regressions before they merge.
README score badge
A live, always-current badge for your README showing the verdict of your most recent scan.
Dependency vulnerability scanning
npm audit for Node, OSV.dev for Python — known-vulnerable packages surface automatically, no extra tooling to install.
Conversational PR chat
@-mention the bot in any PR or MR thread and it replies in context, using the diff to answer follow-up questions.
Engineered to control AI spend
AI credits, not raw request counts. A three-stage pipeline keeps the LLM off the critical path until the code actually warrants it.
ESLint, TypeScript diagnostics, complexity, formatting, and secret scanning run first — no AI, no cost.
Only the functions Stage 1 flags as risky go to an LLM, with just the relevant code and types — not the whole file.
Re-scanning unchanged code — the common case across repeated repo scans — costs nothing and returns instantly.
Start free, upgrade when you need more
Questions teams ask before they adopt it
Short answers to the questions that matter when you are deciding whether to trust a review tool in your workflow.
How does Audit Bench Ai control AI costs?
Free local checks run first, and only risky code is escalated to an LLM. Cached scans are free, so repeated reviews do not burn credits.
Which git providers are supported?
GitHub and GitLab are supported natively for pull request and merge request review.
What frameworks does it understand?
It understands common web stacks including React, Next.js, Node.js, NestJS, Python, FastAPI, Django, Laravel, Spring Boot, Supabase, Deno, and Firebase.
Review your first repository free
No credit card required. Connect GitHub or GitLab, or upload a .zip, and see what an audit finds in your own code.