Quality Assurance / Compliance Review Agent

AI compliance agent generating clause-level tracked-change findings.

An AI review workflow that checks uploaded reports against established standards and returns clause-level tracked changes with a clear explanation for every finding.

The problem

Manual compliance and audit reviews against established standards were slow and inconsistent between reviewers, with each report taking about a week to complete.

My approach

Built an agent that checks uploaded reports against an established standards dataset, then generates a findings report with clause-level tracked changes and AI-written comments explaining every flagged issue.

How it's built

A Python and FastAPI backend deployed on AWS handles report ingestion and comparison against the standards dataset. Claude powers the review and comment generation, returning findings tied to individual clauses for a traceable review workflow.

Key features

Report upload and comparison against a standards dataset
Clause-level tracked-change findings
AI-generated comments for every finding
Consistent, repeatable review logic across reports

Challenges & Solutions

Broad AI feedback was not precise enough for reviewers who needed to see exactly which clause triggered a finding.
Grounded each check in the standards dataset and returned findings as clause-level tracked changes with an explanation attached.
Reviewer judgement varied between reports, making outcomes difficult to compare and slowing final sign-off.
Encoded the review as one repeatable agent workflow so every report is evaluated against the same standards and output structure.

Results

50%shorter audit turnaround, from about one week to roughly half that.
2xfaster review process with more consistent outcomes.

Implementation stack

Tech Stack

Backend
- Python
- FastAPI
Cloud
- AWS
AI
- Claude (Anthropic)