Specialist underwriters were acting as highly-paid data-entry clerks, spending 45 to 90 minutes of administrative labor per case manually downloading unstructured PDFs from their CRM. They were forced to visually scan six months of commercial bank statements and tax returns to identify income streams, manually re-keying the data line-by-line into the proprietary "Case Flow" platform.
Commercial Finance
PDF Triage
The Bleeding Neck
Specialist underwriters acting as data-entry clerks.
Architecture & Prototype
Qeiva API Bridge: Commercial & Bridging Parser
1. 'Case Flow' Inbound Documents (Unstructured)
Qeiva deployed a custom Python OCR pipeline that intercepts unstructured PDFs the millisecond they hit the Case Flow portal. The architecture algorithmically extracts the financial data, formats it into standardized JSON payloads, and pushes it directly into the CRM via REST API.
The ROI
2 Min
Processing Time
<0.1%
Error Rate
3x
Volume Scalability
Before: 45 to 90 minutes of manual labor per case.
After: Compressed into under 2 minutes of automated background processing.
Net Impact: Dropped transposition errors to <0.1% and allowed the commercial team to handle triple the volume without scaling administrative headcount.