vishal maurya

RAG Development and Document AI

I build RAG applications that let people ask questions over PDFs, business documents and private knowledge bases, with answers grounded in your sources.

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What I build

  • Natural-language search and Q&A over PDFs and business documents
  • Private knowledge base assistants for teams
  • Document processing pipelines for extraction and summarisation
  • Answers with source references, so users can verify them

How it is built

A production RAG system includes upload validation, text and table extraction, structure-aware chunking, embeddings, a vector index, retrieval, access checks, answer generation with citations, and evaluation on real questions. I explain the design choices in how to build a secure RAG system for large PDF documents.

Related services: AI agent development, AI automation and AI app development.

Pricing depends on scope. Tell me about your documents.

Common questions

What is a RAG application? Retrieval-augmented generation finds the relevant passages in your documents and gives them to a language model, so answers are based on your data and can cite sources.

How much does RAG development cost? It depends on document volume and types, extraction difficulty such as tables and scans, access control, UI and model usage costs. I quote after reviewing sample documents.

Can it handle tables and scanned PDFs? Yes, with table-aware extraction and OCR where needed, tested on your representative documents.

Is my data kept private? Documents stay in your controlled storage, access is checked per user, and secrets are kept server-side. I discuss model provider options with you.

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