The firm operated with a highly unusual 1:2 broker-to-case-manager ratio because the back office was suffocating under complex unstructured data. Case managers were forced to act as human OCR scanners—spending up to 3 hours per case manually reading 50-100 page PDFs containing foreign tax returns, sprawling 15-property portfolio ASTs, and HMO licenses. This grueling manual extraction was required just to calculate aggregate Interest Cover Ratios (ICRs) before underwriting could begin, obliterating the profit margins of their top-line growth.
Specialist Mortgage Triage
& PRA Compliance Automation
The Bleeding Neck
Case managers forced to act as human OCR scanners.
Architecture & Prototype
Qeiva API Bridge: Bespoke CRM Ingestion Engine
1. Unstructured Broker Upload (Specialist Finance)
Qeiva deployed a bespoke machine-learning OCR architecture utilizing Computer Vision and Natural Language Processing (NLP) designed explicitly for complex UK property finance. The Python engine automatically identifies document boundaries in massive PDF portfolios, maps precise bounding boxes to critical financial fields, and pushes clean JSON payloads directly into Mortgage Lane’s award-winning custom CRM via API.
The ROI
80%
Extraction Time Reduced
Protected
Profit Margins
3x
Capacity Multiplication
Before: 3 hours per case manually reading 50-100 page PDFs.
After: Reduced to a 15-minute human-in-the-loop verification task.
Net Impact: Prevented the need to hire two new administrators for every single new broker onboarded during their aggressive 2026 expansion, and allowed a single case manager to comfortably handle triple the specialist case volume without burnout.