Document Processor
92% Extraction Accuracy for Legal Document Processing
The Challenge
A legal services firm processing 2,000+ documents monthly hit 68% accuracy on their previous OCR, requiring extensive manual correction that negated automation benefits.
Our Approach
A multi-stage pipeline classified documents by quality, routed degraded scans through GPT-4 Vision verification, and surfaced confidence scores per extracted field for a human review queue.
The Execution
Delivered across 8 weeks with the following technology stack:
How we worked
Discovery
Deep-dive into existing systems, constraints, and stakeholder interviews.
Architecture
Design the system blueprint, data models, and integration points.
Prototype
Ship a working slice end-to-end to validate assumptions.
Build
Full development with weekly demos and continuous integration.
Deploy
Production rollout with monitoring, rollback plans, and training.
Scale
Performance tuning, documentation, and knowledge transfer.
The Results
- 92% field-level extraction accuracy (up from 68%)
- 15x faster processing (45 min → 3 min per document)
- Firm took on 30% more client work without additional staff
Architecture Overview
The Future
This engagement established a foundation we continue to build on. The systems we shipped are now handling production workloads, and the architecture we designed is positioned for the next phase of scale.
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.