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Upload PDFs and documents for automated parsing, classification, embedding, and knowledge extraction.
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AI-powered document intelligence platform for autonomous knowledge discovery and analysis.
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Real-time document processing with OCR, entity extraction, vector indexing, and graph construction.
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Automatically detect policy conflicts, compliance gaps, missing approvals, and operational risks.
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Ask questions in plain English and receive citation-backed answers grounded in source documents.
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Browse indexed documents, extracted entities, metadata, and cross-document relationships.
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AI-generated alerts surface critical findings before they become business problems.
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Interactive knowledge graph connecting policies, teams, systems, and compliance relationships.
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Visualize document dependencies, superseded policies, implementation links, and operational impacts.
🧠 Cognivault
Inspiration
Organizations generate thousands of documents such as policies, invoices, reports, contracts, and circulars. While these documents contain valuable knowledge, finding the right information quickly is often difficult and time-consuming.
We wanted to build a system that goes beyond simple document storage and search. Our goal was to create an AI-powered platform that automatically understands documents, connects related information, and helps users retrieve knowledge instantly.
What it does
Cognivault is an AI-powered document intelligence platform that transforms unstructured documents into a connected knowledge network.
Users can upload PDFs, reports, policies, contracts, invoices, and other documents. The platform automatically:
- Extracts and organizes information
- Builds relationships between documents
- Creates interactive knowledge graphs
- Enables semantic search and AI-powered questioning
- Provides grounded answers using retrieved evidence
- Detects risks, conflicts, and operational insights
Whether used by students, businesses, or government organizations, Cognivault helps users find information faster and make better decisions.
How we built it
Frontend
- React
- TypeScript
- Tailwind CSS
- React Flow
Backend
- FastAPI
- Python
- SQLAlchemy
- SQLite
AI & Intelligence
- RAG (Retrieval-Augmented Generation)
- Semantic Search
- OCR Processing
- Knowledge Graphs
- NetworkX
- ChromaDB
- BM25 Retrieval
- Groq LLM APIs
Challenges we ran into
- Building reliable document relationship extraction
- Reducing hallucinations in AI-generated answers
- Designing scalable knowledge graph structures
- Connecting retrieval systems with graph reasoning
- Deploying and integrating frontend and backend services
Accomplishments that we're proud of
- Built a complete document intelligence platform
- Implemented knowledge graph visualization
- Developed grounded AI-powered querying
- Created autonomous alert and insight generation
- Successfully deployed the application for real-world usage
What we learned
Through Cognivault we learned:
- Knowledge graph engineering
- Retrieval-Augmented Generation (RAG)
- Semantic search architectures
- AI reliability and trust validation
- Full-stack deployment and integration
What's next for Cognivault
- Multi-user collaboration
- Real-time document monitoring
- Advanced compliance analysis
- Multilingual document support
- Enterprise workflow automation
- Predictive intelligence and analytics
Built With
- bm25
- chromadb
- css
- fastapi
- graphs
- groq
- knowledge
- networkx
- ocr
- python
- rag
- react
- search
- semantic
- sqlalchemy
- sqlite
- tailwind
- typescript
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