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2025-12-16 07:16:44 Melisha Dsouza: Update
Projects/Audit-matic.md ..
@@ 1,6 1,6 @@
**<center><h1>Audit-matic</h1></center>**
- **Project**: AuditMatic
+ ## **Project**: AuditMatic
**Tagline:**
Automating CMMI compliance by bridging the gap between business requirements and development artifacts.
@@ 32,9 32,9 @@
##### **What does it do?**
AuditMatic transforms the audit process into a fast, reliable, and AI-driven pipeline by integrating:
- 1. **Phase 1:** Automated document existence and basic entity checks.
- 2. **Phase 2:** Deep **Content Validation** using the **RAG Pipeline** and the Gemini LLM.
- 3. **Phase 3:** **Cross-Platform Ecosystem Coverage** (Jira, GitLab, Bitbucket) for universal traceability.
+ * [ ] **Phase 1:** Automated document existence and basic entity checks.
+ * [ ] **Phase 2:** Deep **Content Validation** using the **RAG Pipeline** and the Gemini LLM.
+ * [ ] **Phase 3:** **Cross-Platform Ecosystem Coverage** (Jira, GitLab, Bitbucket) for universal traceability.
##### **How does it work?**
@@ 54,22 54,21 @@
> ### **Technology Stack**
- **AI/ML Models:** Gemini API (gemini-2.5-flash, embedding-001), OpenAI API (gpt-4o-mini, text-embedding-3-large), Xenova/Transformers (Local Embeddings), Custom Rule-Based Expert System, spaCy (Pre-processing)
+ * [ ] **AI/ML Models:** Gemini API (gemini-2.5-flash, embedding-001), OpenAI API (gpt-4o-mini, text-embedding-3-large), Xenova/Transformers (Local Embeddings), Custom Rule-Based Expert System, spaCy (Pre-processing)
- **LLM:** Gemini (Google) and GPT (OpenAI), managed via a multi-LLM Strategy Pattern.
+ * [ ] **LLM:** Gemini (Google) and GPT (OpenAI), managed via a multi-LLM Strategy Pattern.
- **Frameworks & Libraries: ** jira, multer
+ * [ ] **Frameworks & Libraries: ** jira, multer
- **Backend:** Node.js v24.x with Express.js v5.1.0 (Built with TypeScript), ts-node, nodemon.
+ * [ ] **Backend:** Node.js v24.x with Express.js v5.1.0 (Built with TypeScript), ts-node, nodemon.
- **Frontend:** React v18.2.0 (Built with TypeScript), Redux Toolkit (RTK) (State Management).
+ * [ ] **Frontend:** React v18.2.0 (Built with TypeScript), Redux Toolkit (RTK) (State Management).
- **Databases:** PostgreSQL 14+ (Primary DB), pgvector v0.2.1 (Vector Search Extension), Redis v5.9.0 (Caching, Session Mgmt).
+ * [ ] **Databases:** PostgreSQL 14+ (Primary DB), pgvector v0.2.1 (Vector Search Extension), Redis v5.9.0 (Caching, Session Mgmt).
- **Deployment & Tools:** Git, GitHub, Docker, Jest (Testing), ESLint (Linting).
-
- **APIs Used:** Jira API (Phase 3), GitLab API (Phase 3), Bitbucket API (Phase 3), Google Drive API, Google OAuth 2.0 API.
+ * [ ] **Deployment & Tools:** Git, GitHub, Docker, Jest (Testing), ESLint (Linting).
+ * [ ] **APIs Used:** Jira API (Phase 3), GitLab API (Phase 3), Bitbucket API (Phase 3), Google Drive API, Google OAuth 2.0 API.
> ### **Project Architecture**
@@ 95,23 94,23 @@
##### **Phase 1:** Foundation & Entity Extraction (Completed)
- **Goal:** Prove the concept of automation by replacing manual document checks.
+ * [ ] **Goal:** Prove the concept of automation by replacing manual document checks.
- **Key Achievement:** Implemented basic checks for document existence using simple Regex and spaCy for entity extraction.
+ * [ ] **Key Achievement:** Implemented basic checks for document existence using simple Regex and spaCy for entity extraction.
##### **Phase 2: **Content Validation & AI Pivot (Current Focus)
- **Goal:** Move from "existence" to "content validation."
+ * [ ] **Goal:** Move from "existence" to "content validation."
- **Key Achievement:** Developed and implemented the RAG pipeline and Multi-LLM Strategy to semantically validate document content against live data.
+ * [ ] **Key Achievement:** Developed and implemented the RAG pipeline and Multi-LLM Strategy to semantically validate document content against live data.
##### **Phase 3:** Ecosystem Expansion & Automation (Future Roadmap)
- **Short-term Goal (P3):** Integrate the Bitbucket API for unified code traceability.
+ * [ ] **Short-term Goal (P3): **Integrate the Bitbucket API for unified code traceability.
- **Medium-term Goal (P3):** Implement AI-driven Gap Filling (LLM suggests missing content or remediation steps).
+ * [ ] **Medium-term Goal (P3):** Implement AI-driven Gap Filling (LLM suggests missing content or remediation steps).
- **Long-term Goal (P3):** ML-based Document Categorization for dynamic rule application across different document types.
+ * [ ] **Long-term Goal (P3):** ML-based Document Categorization for dynamic rule application across different document types.
> ### **Repository & Live Demo**
@@ 119,8 118,10 @@
**Live Demo:** https://demo4.enovate-it.com/
- **Screenshots / Demo Video:**
+ > ### **Screenshots / Demo Video**
**Figma link:** https://www.figma.com/make/hgPLDKHEFYH3ratyLlUx96/Create-AI-Design-Prompt?node-id=0-1&p=f&t=cC5O3FcQiEGFsTDr-0&fullscreen=1
+ ***
+
**Categories**: #project #ai-hackathon-2025 #category-compliance #category-ai
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