AI Document Analysis & RAG Systems

AI Document Analysis & RAG

We build Retrieval-Augmented Generation (RAG) systems that teach AI your company’s contracts, reports, manuals, and knowledge base. Create an AI assistant that can understand, answer questions about, and summarize thousands of pages in seconds.

RAG Architecture

Document Processing: Automatically parse PDFs, Word and Excel files, PowerPoint presentations, and web pages; split them into chunks; and store them in a vector database using semantic embeddings.

Vector Search: Convert a user’s question into an embedding and find the most relevant document passages in milliseconds. We use Pinecone, pgvector, or Weaviate.

LLM Response Generation: Send the retrieved context to GPT-4 or Claude to produce grounded answers with fewer hallucinations. Every answer cites its source document and page number.

Use Cases

Legal Document Analysis: Automatically identify key clauses, obligations, and risks in contracts.

HR Policy Assistant: An internal assistant that instantly answers employees’ questions about policies, leave, and procedures.

Technical Documentation: A developer assistant that understands product manuals and API documentation.

Financial Report Analysis: Automatically extract insights from balance sheets, income statements, and audit reports.