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.
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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.
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**The Problem
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> ### **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.
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**Our AI Solution
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> ### **Our AI Solution**
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**What does it do?**
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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.
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How does it work?
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**How does it work?**
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The architecture has three main components:
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React Web App: User interface for uploading documents.
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1. **React Web App:**
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* User interface for uploading documents.
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Express.js Backend: Receives the file and triggers the AI pipeline.
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2. **Express.js Backend:**
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* Receives the file and triggers the AI pipeline.
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Python AI Core:
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3. **Python AI Core:**
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* Uses PyMuPDF to extract text from documents.
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* Uses spaCy for NLP to detect Jira IDs and commit hashes.
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* Fetches relevant data via Jira and GitLab APIs.
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* Runs compliance checks (Traceability, Accountability, Completeness) via a rule-based expert system.
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* Returns a structured JSON report to the web app for display.
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Uses PyMuPDF to extract text from documents.
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**What makes it innovative?**
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Uses spaCy for NLP to detect Jira IDs and commit hashes.
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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.
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Fetches relevant data via Jira and GitLab APIs.
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> ### **Technology Stack**
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Runs compliance checks (Traceability, Accountability, Completeness) via a rule-based expert system.
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**AI/ML Models:** spaCy (NLP/NER), Custom Rule-Based Expert System
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Returns a structured JSON report to the web app for display.
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.
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**Backend:** Express.js (Node.js)
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**Technology Stack
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**Frontend:** React
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AI/ML Models: spaCy (NLP/NER), Custom Rule-Based Expert System
Medium-term Goal: Train a custom NLP model with spaCy to recognize non-standard internal project identifiers for better accuracy.
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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.
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**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.
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**Repository & Live Demo
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> ### **Repository & Live Demo**
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GitHub Repository: [Link to your code repo here]
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Live Demo: [Link to live demo or video walkthrough here]
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**GitHub Repository:** [Link to your code repo here]
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Screenshots / Demo Video
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**Live Demo:** [Link to live demo or video walkthrough here]