Audit-matic
Project: AuditMatic
Tagline: Automating CMMI compliance by bridging the gap between business requirements and development artifacts.
Team: Fusion
Project Status: Planning
Overview
AuditMatic is an AI-powered system designed to automate the CMMI compliance auditing process. For many development teams, manually ensuring engineering work aligns with business requirements is slow, error-prone, and resource-intensive. AuditMatic solves this problem by intelligently bridging the gap between business documents and development tools such as Jira and GitLab.
The system ingests unstructured business requirement documents, uses NLP to extract key identifiers like ticket numbers and commit hashes, and cross-validates this data with live project data. This automated approach produces detailed compliance reports in minutes, saving time and reducing human error.
By combining off-the-shelf NLP models with a custom rule-based expert system, AuditMatic transforms a manual, high-effort audit process into a fast, reliable, and AI-driven pipeline.
The Problem
CMMI compliance auditing requires auditors to read through complex business documents and manually verify alignment with tickets in Jira and commits in GitLab. This process is slow, costly, and prone to human error, which can result in failed audits and non-compliance.
Our AI Solution
What does it do? AuditMatic is a web application that automatically checks CMMI compliance by cross-validating uploaded business requirement documents against live data from Jira and GitLab.
How does it work? The architecture has three main components:
React Web App: User interface for uploading documents.
Express.js Backend: Receives the file and triggers the AI pipeline.
Python AI Core:
Uses PyMuPDF to extract text from documents.
Uses spaCy for NLP to detect Jira IDs and commit hashes.
Fetches relevant data via Jira and GitLab APIs.
Runs compliance checks (Traceability, Accountability, Completeness) via a rule-based expert system.
Returns a structured JSON report to the web app for display.
What makes it innovative? AuditMatic replaces a manual audit process with an automated AI-driven pipeline. It combines NLP, API integration, and rule-based logic to solve a high-value business problem efficiently.
Technology Stack
AI/ML Models: spaCy (NLP/NER), Custom Rule-Based Expert System Frameworks & Libraries: PyMuPDF, python-docx, jira, python-gitlab, multer Backend: Express.js (Node.js), Python Frontend: React Databases: N/A for pilot phase Deployment & Tools: Git, GitHub, Docker! APIs Used: Jira API, GitLab API
**Project Architecture
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Challenges & Learnings
Challenges:
Creating robust NLP patterns to handle document variations.
Ensuring smooth orchestration between React, Node.js, and Python.
Handling API rate limits and error management for Jira and GitLab.
Learnings: Integrating NLP with rule-based expert systems.
Handling unstructured data efficiently.
Building a polyglot microservice architecture.
Future Roadmap
Short-term Goal: Implement core compliance checks (Traceability, Accountability, Completeness) using regex-based entity recognition. Medium-term Goal: Train a custom NLP model with spaCy to recognize non-standard internal project identifiers for better accuracy. Long-term Goal: Use machine learning to automatically categorize different types of requirement documents (e.g., Test Plan, Risk Plan, Functional Spec) and apply different rule sets.
Repository & Live Demo
GitHub Repository: [Link to your code repo here] Live Demo: [Link to live demo or video walkthrough here]
Screenshots / Demo Video
Categories: #project #ai-hackathon-2025 #category-compliance #category-ai