codebase-review-swarm

To run an AI code review without hallucinated findings, use a quote-grounded review process that enforces evidence validation and coverage closure. This method separates defect detection from enhancement opportunities, requiring deterministic checks before subjective analysis to produce verified, actionable reports.

To audit a full codebase for security and accessibility issues, initiate a multi-track codebase audit at your repository root. This process applies exhaustive depth to selected tracks, generating a machine-readable review artifact and a final verified report with exact evidence without modifying source files.

Quote-grounded evidence in a codebase audit is a validation mechanism that eliminates unverified AI code review outputs by enforcing strict evidence matching. It applies multi-stage reviewer and critic validation to all findings, ensuring only verified, actionable results are produced for full-repo scenarios.

Yes, this code review workflow supports codebases of any size or tech stack. The review process maintains a non-diluting depth guarantee, meaning selected audit tracks receive the same exhaustive depth whether run alone or as part of a multi-track review without sampling or shallow checks.

Yes, you can run a supply chain review separately from a full codebase audit. The multi-track audit support allows you to run focused reviews for supply chain, security, QA, performance, AI-slop, or enhancement opportunities independently without diluting the review depth.

Standard AI code review limitations include unverified, hallucination-prone outputs and shallow checks. A quote-grounded review overcomes these constraints by mandating coverage closure and multi-stage validation, ensuring every finding is backed by exact evidence and separating defect detection from enhancement opportunities.