How we built an AI-powered document processing pipeline in 8 weeks
The problem
Our consulting team processes thousands of documents per month: invoices, contracts, technical specifications, and project reports. Manual data extraction was consuming 20+ hours per week of senior engineer time — work that was repetitive, error-prone, and demoralising.
We decided to build an automated pipeline. The goal was not to replace human judgment but to eliminate the manual drudgery of extracting structured data from unstructured documents.
The pipeline architecture
The system has four stages:
- OCR and pre-processing: Tesseract for scanned documents, native text extraction for digital PDFs. Pre-processing handles rotation, noise reduction, and page segmentation.
- Classification: A fine-tuned classifier routes documents to the appropriate extraction pipeline. Invoice? Contract? Technical spec? Each type has its own extraction logic.
- Extraction: For structured documents (invoices), we use rule-based extraction with regex patterns. For semi-structured documents (contracts), we use a fine-tuned language model to extract key entities: parties, dates, obligations, and financial terms.
- Validation: Extracted data is validated against business rules. Missing fields, inconsistent dates, and unusual amounts trigger human review.
Results
The pipeline processes 85% of documents without human intervention. Processing time dropped from 45 minutes per document to under 30 seconds. The remaining 15% — typically handwritten notes, unusual formats, or low-quality scans — are flagged for human review with pre-extracted context that speeds up the manual process.
The team now spends their time on analysis and decision-making rather than data entry. That shift in focus has been more valuable than the time savings alone.