Case Study

AI-Based Document Processing System

Building an automated document processing engine to extract and classify data from complex legal and financial documents using OCR and LLMs.

Role
AI Architect
Timeline
6 Months
Industry
Legal / Fintech
Focus
Python

Problem Breakdown

The client's team was manually reviewing thousands of complex multi-page documents monthly, looking for specific legal clauses, which was slow and highly prone to error.

Architecture Decisions

  • /AWS Textract for robust layout-aware OCR
  • /LLM ensemble approach for high-precision data extraction
  • /Asynchronous processing queue to handle unpredictable document volumes

Trade-offs

  • ¬High per-document processing cost for LLM calls
  • ¬Managing confidence scores for automated vs. manual review loops
  • ¬Complex multi-step pipeline requiring significant observability

Key Outcomes

  • Automated 80% of the manual document review and triage process.
  • Reduced document processing time from days to less than 10 minutes.
  • Improved data extraction accuracy over manual entry by 25%.
  • Implemented searchable document index using Elasticsearch for rapid discovery.
PythonPyTorchAWS TextractOpenAIElasticsearch

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