Enterprise Document Intelligence Platform

Unified enterprise workspace for document search, AI queries, conflict detection, and document management.
Multilingual interface enabling document interaction in English and and all the other language
Version-aware document editor with controlled Before/After changes and immutable version creation.
Evidence-grounded RAG for cross-document Q&A with relevant source retrieval and similarity-based matching.

Inspiration

Modern organizations work with information distributed across PDFs, DOCX files, CSVs, and scanned documents. Finding the right information manually is time-consuming, while inconsistencies between documents can easily be overlooked.

We wanted to build a single intelligent platform that makes enterprise documents easier to search, understand, compare, analyze, and manage.

What it does

Enterprise Document Intelligence Platform is an AI-powered system that allows users to upload and interact with information from multiple document formats.

Users can:

Upload PDF, DOCX, CSV, and image-based documents
Ask questions about their uploaded documents using RAG
Extract text from scanned documents using OCR
Detect inconsistencies across documents
Visualize numerical data through interactive charts
Ask questions using voice input
Edit documents and compare Before/After versions
Manage document versions and approval workflows
Use role-based access to manage enterprise documents

How we built it

The frontend was built using React, with a backend API layer handling document processing and application functionality.

For document ingestion, we use format-specific extraction:

PyPDF for PDF documents
python-docx for DOCX files
Pandas for CSV files
WinOCR, with Tesseract as a fallback, for image-based and scanned content

The extracted information is converted into a common text representation and divided into overlapping chunks before entering the retrieval pipeline.

Our RAG system combines keyword matching, TF-IDF relevance, and cosine similarity to retrieve the most relevant context for a user's question. The retrieved context is then provided to the generation layer to produce a grounded response.

For numerical information, structured data can be transformed into chart data and visualized using React and Recharts.

Voice interaction uses the browser's Speech Recognition API, allowing users to speak their questions instead of typing them.

The platform also includes document editing, Before/After comparison, version management, approval workflows, and role-based access control.

Challenges we ran into

One of our biggest challenges was handling different document formats and bringing their extracted information into a common processing pipeline.

Another challenge was information retrieval. A question does not always contain the exact words present in a document, so relying only on keyword matching would not be sufficient. We therefore combined multiple retrieval techniques, including TF-IDF and cosine similarity.

We also had to handle scanned documents where normal text extraction is not possible, which required an OCR-based approach.

Finally, we needed to make the system useful beyond simple question answering by supporting comparison, inconsistency detection, visualization, voice interaction, and document workflows.

Accomplishments that we're proud of

Built a unified document processing pipeline for multiple file formats.
Implemented a multi-signal RAG retrieval system using keyword matching, TF-IDF, and cosine similarity.
Integrated OCR for scanned and image-based documents.
Added interactive numerical data visualization.
Implemented voice-based document queries.
Added document comparison, versioning, and approval workflows.
Successfully presented the project at HackStreak 3.0.
🏆 Won the Third Year Track at HackStreak 3.0.

What we learned

This project helped us understand how to build an end-to-end AI application rather than simply integrating an AI model.

We gained practical experience with document processing, OCR, information retrieval, RAG, similarity-based search, API development, frontend visualization, and enterprise-oriented workflows.

We also learned how important it is to design an AI system around the actual problem instead of relying on a single technique.