Case Study 02

Document Intelligence.

Automated extraction, classification, validation, and retrieval for structured and unstructured PDF/DOCX content using OCR, NLP, transformers, and semantic search.

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document_pipeline.py

PDF / DOCX
  → OCR + parsing
  → NLP cleanup
  → classify fields
  → validate output
  → embeddings
  → search API

Workflow

Convert messy documents into searchable knowledge.

Extract

Capture text and metadata from documents.

Clean

Normalize noisy text for downstream NLP.

Classify

Use transformer models for document understanding.

Validate

Check extracted fields before system handoff.

Index

Generate embeddings for semantic retrieval.

Serve

Expose document intelligence through APIs.

Impact

Reported outcomes

Less manual work, more reliable retrieval.

65% less manual processing32% accuracy improvement40% shorter release cycles

Stack

Document AI

Python, FastAPI, Hugging Face, AWS, PostgreSQL, ChromaDB, OCR.

Built around practical automation and searchable enterprise knowledge.