BFSI · RAG Pipeline · OCR + LLM · Document Intelligence · Source Traceability
Bond and legal PDFs of 1,000–2,000+ pages with no text layer — manual review was completely impractical.
Dense, multi-level tabular data embedded across hundreds of pages required specialized extraction.
Direct LLM usage over legal and financial content risked unverifiable, non-grounded answers.
Users needed full visibility into exactly which page and section each answer was derived from.
PaddleOCR extracts text and complex tables from scanned PDFs; context-aware chunking preserves table structures.
Text embeddings for granular matching + summary embeddings for broader context, with metadata tagging.
Semantic similarity, keyword retrieval, and metadata filtering combined with a custom ranking layer.
Responses generated strictly from retrieved chunks — UI highlights exact source pages and passages.
Pages processed — fully scanned, previously unsearchable.
Hallucination risk — answers grounded in retrieved content.
Source traceability — exact page highlighted for every answer.
RAG architecture — foundation for legal and finance AI.