Multimodal document intelligence platform combining TensorFlow computer vision for page layout segmentation with an offline, local LLM RAG pipeline in ChromaDB for private document querying.
Organizations process large volumes of unstructured PDF documents daily—invoices, financial audits, research papers, and technical manuals. Manually categorizing documents, extracting tabular data, and verifying specific clauses across multi-page files is slow and error-prone. Furthermore, sending confidential enterprise documents to public cloud AI APIs creates unacceptable data privacy and compliance risks.
Document AI pairs deep learning computer vision (TensorFlow) with a fully offline LLM retrieval-augmented generation (RAG) pipeline. It automatically segments PDF page layouts, classifies document categories, extracts tabular structures, and indexes chunked embeddings into ChromaDB for instant, hallucination-resistant Q&A with complete local data sovereignty.
TensorFlow computer vision models automatically categorize PDFs into invoices, legal contracts, reports, and IDs.
Chunks document text and indexes embeddings into ChromaDB for instant, hallucination-free local retrieval Q&A.
Extracts text regions, tables, embedded images, and handwritten annotations with high precision.
Generates multi-bullet executive summaries and key takeaway highlights for long research papers and reports.
Deep learning vision architecture for page segmentation and document layout classification.
Offline semantic vector store and local language model runner for private document Q&A.
Natural language preprocessing, tokenization, text cleaning, and PDF page parsing.
Interactive web dashboard with document upload drag-and-drop, PDF viewer, and live chat.