The Challenge
Compliance teams were spending 20+ hours weekly manually extracting data from unstructured PDF documents. The process was error-prone, inconsistent, and became harder as document volumes increased.
The RAG Pipeline
We built a Retrieval-Augmented Generation system using LangChain and OpenAI embeddings. Documents are chunked, embedded into a vector database, and queried with context-aware prompts that extract structured metadata accurately.
Validation Layer
An automated validation worker cross-references extracted data against compliance rules stored in a PostgreSQL knowledge base. Flagged documents are routed to human reviewers with highlighted confidence scores.
Impact
Processing time reduced from 45 minutes to 3 minutes per document. Accuracy improved to 97.2%, and the team now handles 5x the document volume with the same headcount.