Intelligent Document Processing System
Problem
A client in environmental engineering had a large volume of scientific PDFs and scanned documents — lab reports, environmental data, research publications — that needed to be searchable and structured, but the content was sensitive enough that sending it to third-party cloud services wasn't acceptable.
What I Built
As primary developer and original architect, I built the full pipeline: documents come in through an upload step, go through multi-engine OCR (supporting 50+ languages to handle the client's mixed-language document set), then structured extraction with NLP, storage in PostgreSQL, semantic vector embeddings for meaning-based lookup, and a search frontend on top.
Technical Decisions
The whole system runs with no cloud dependency by design — every stage, from OCR through embeddings to search, runs on infrastructure the client controls. That was a deliberate architectural constraint from day one, driven by privacy and GDPR compliance requirements, not a cost decision, and it shaped the choice of every component in the pipeline.
Result
A working end-to-end pipeline — from raw scanned document to searchable, structured data — built and operated without any cloud dependency, for a client whose data sensitivity made that a hard requirement.