CLIENT
Legal Services Firm (Anonymized)
TIMELINE
8 weeks
SERVICES
Document AI, Data Extraction, Pipeline Architecture
STACK
GPT-4 Vision, Tesseract OCR, Apache Airflow, PostgreSQL, MinIO
// 01
TheProblem
2,000+ documents monthly. 68% OCR accuracy. Paralegals buried in data entry.
A mid-size legal services firm processing 2,000+ documents monthly was bottlenecked by manual data extraction. Contracts, filings, and compliance documents arrived in inconsistent formats — scanned PDFs, photographed pages, legacy Word documents, and handwritten annotations. Paralegals spent 60% of their time on data entry rather than substantive legal work.
Previous OCR solutions achieved only 68% accuracy on their document mix, requiring extensive manual correction that negated automation benefits. The firm needed a system that could handle their diverse document types while maintaining the accuracy standards required for legal proceedings.
// 02
OurApproach
A multi-stage pipeline combining traditional OCR with vision-language model verification.
We built a multi-stage document processing pipeline that combines traditional OCR with vision-language model capabilities. The first stage classifies incoming documents by type and quality, routing each to the optimal extraction path. High-quality digital PDFs go through direct text extraction, while degraded scans and images are processed through an enhanced OCR pipeline with GPT-4 Vision verification.
The extraction engine uses document-type-specific prompting strategies. For contracts, it identifies and extracts clause structures, party information, dates, and financial terms. For filings, it maps to regulatory schema templates. Each extraction includes confidence scores at the field level, flagging low-confidence results for human review.
The pipeline orchestration runs on Apache Airflow with parallel processing lanes, handling burst loads during filing deadlines. All documents are stored in MinIO with full version history and audit trails. We implemented a feedback loop where human corrections automatically improve extraction templates, creating a continuously improving system.
Quality gates at each pipeline stage ensure that no document proceeds without meeting accuracy thresholds. Failed documents are routed to a review queue with pre-extracted context, making manual processing 5x faster than starting from scratch.
// 03
TheResult
92% extraction accuracy. 15x faster processing. Paralegals freed for real legal work.
The pipeline achieved 92% field-level extraction accuracy across the firm's document mix, up from 68% with their previous OCR solution. For high-quality digital documents, accuracy reaches 98.5%. Processing throughput increased 15x, with the average document completing extraction in 3 minutes compared to 45 minutes of manual work.
Paralegals now spend 80% of their time on substantive legal analysis rather than data entry. The firm has been able to take on 30% more client work without hiring additional support staff. The continuous improvement loop has pushed accuracy up 4 percentage points in the six months since deployment, as the system learns from corrections.
The solution has been expanded to handle incoming client correspondence, automatically extracting action items and linking them to case files.
// IMPACT
extraction accuracy on unstructured documents
from discovery to scaled production
faster document processing vs. manual review
“What used to take a paralegal an entire day now completes in 40 minutes with higher accuracy. The ROI was obvious within the first month.”