PE-BACKEDNational Legal Services FirmLegal
Automating UCC Document Extraction and Classification
- 85%
- reduction in manual processing time
- The challenge
- Paralegals spent 20+ hours per week manually extracting, reviewing, and classifying hundreds of UCC filings from a state database. Error rates were climbing as volume increased, and substantive legal work was being displaced by data entry.
- What we built
- Built an end-to-end pipeline that uses Python to retrieve and extract filing data, Claude Sonnet to classify standard records, and Claude Opus to handle complex edge cases. A custom web interface routes the results to paralegals for review, preserving human control over exceptions. The complete system runs within the firm's existing Azure infrastructure.
Supporting outcomes
- 96.2%
- classification accuracy on standard filings
- 45 sec
- per-filing processing time, down from 12 minutes
- 0
- compliance incidents in first 3 months
- Timeline
- 6 weeks
- Phases
- 4
Built with
- Python
- Claude Sonnet
- Claude Opus
- Azure
- Custom Web UI