Case Study: Document intelligence
What challenges did the Bank Statement OCR Processor address?
TL;DR: Financial records come in wild formats (scanned PDFs, cell phone pictures, chaotic tables). Reviewing thousands of statements manually was slow, costing hours of human labor and averaging a 4% data entry error rate.
How did we design and implement the engineering approach?
- Built a concurrent Go ingestion pipeline to parse, pre-process, and batch upload document pages.
- Integrated Gemini 2.5 Flash via structured JSON outputs to perform OCR corrections and interpret transaction details.
- Engineered strict validation schemas to cross-check running balances against listed transactions automatically.
What was the final outcome and performance result?
TL;DR: Automated extraction of 8,500+ statements with 99.1% parsing accuracy, slashing document processing time from 15 minutes down to 12 seconds per file.