FEATURED PROJECT
Intelligent Due Diligence & Risk Analysis Platform
An AI-powered platform that extracts, structures and retrieves information from due diligence documents using PostgreSQL, Node.js and modern search technologies.
Documents → AI Extraction → PostgreSQL → Intelligent Search
PDF, DOCX and XLSX documents are processed, structured and made searchable.
1. The Problem
Organizations conducting due diligence receive large volumes of documents from vendors, contractors and partners.
- Manual review is time-consuming and difficult to scale.
- Critical information can easily be overlooked.
- Information is scattered across multiple documents.
- Similar vendors are evaluated repeatedly with little reuse.
- Teams spend more time searching than analysing.
2. The Solution
An AI-powered platform that extracts, structures and organizes key information from documents and makes it instantly searchable.
- Automates extraction of critical data points.
- Stores structured data in PostgreSQL for reliability.
- Enables keyword and fuzzy search.
- Reduces manual effort and review time.
- Helps teams make faster, data-driven decisions.
3. Architecture
Documents → Document Processing → AI Information Extraction → Structured JSON → PostgreSQL Database
↓
REST API + Search Engine
↓
Web Dashboard
4. Technology Stack
Backend — Node.js, Express.js
Database — PostgreSQL 15+
AI / NLP — OpenAI API, LangChain (planned)
Search — PostgreSQL Full-Text Search, Fuzzy Search, pgvector (roadmap)
APIs — REST API (JSON)
Frontend — EJS, Bootstrap, JavaScript
Tools — Git, GitHub, VS Code, Postman
Cloud / DevOps — Docker (planned), Nginx
5. Key Features
AI Document Extraction — Extracts structured data from diverse document types using advanced AI models.
Intelligent Search — Search across all documents using keywords, fuzzy matching and filters.
PostgreSQL Data Model — Structured, relational storage for fast queries, reporting and data integrity.
REST APIs — APIs for uploading documents, retrieving data, search and integrations.
Scalable Design — Modular architecture built for scale and maintainability.
Future AI Enhancements — Designed to support embeddings, semantic search and RAG-based capabilities.
6. Challenges & Lessons Learned
- Extracting reliable information from inconsistent, unstructured documents was the biggest challenge.
- AI works best when combined with structured validation.
- PostgreSQL is powerful for transactional and search workloads.
- Modular architecture makes future enhancements easier.
- User feedback is critical for improving accuracy.
7. Future Roadmap
- Semantic vector search with pgvector
- Hybrid keyword + semantic search
- AI-generated due diligence summaries
- Automated risk scoring
- Interactive dashboards & analytics
- Multi-user authentication & roles
- Document versioning & audit trail
- Integration with procurement systems
8. GitHub Repository
The complete source code, documentation and development roadmap are available on GitHub.
This project is actively evolving.
Contributions, ideas and feedback are welcome! Feel free to explore the code, raise issues or suggest improvements.