To deliver their promised 48-to-72-hour indicative approvals, the firm relied on a highly sophisticated React and Supabase architecture. However, when self-employed brokers uploaded unstructured PDFs—such as complex tax returns, Business Activity Statements (BAS), and multi-entity P&Ls—standard extraction tools like Azure Document Intelligence failed to map the chaos into Salesforce Finspectra's rigid data models. The automated sync failed, forcing highly-paid credit analysts to spend 48 hours manually reading documents and keying data into Salesforce just to meet SLAs.
Custom CRM Synchronization
& Unstructured Data Parsing
The Bleeding Neck
Standard extraction tools failed to map the chaos.
Architecture & Prototype
Qeiva API: Supabase ➔ Salesforce (Finspectra) Sync
1. React Portal Ingestion (Unstructured ATO & BAS)
Qeiva engineered a custom Python OCR pipeline utilizing computer vision trained specifically on Australian ATO formats and bank statements. The pipeline intercepts the PDFs, extracts the exact financial variables, and pushes a perfectly sanitized JSON payload directly into the Salesforce REST API, automatically updating Finspectra collateral and checklist objects in seconds.
The ROI
48 Hrs
Manual Phase Eliminated
100%
System Stabilization
$330M
Scalable AUM Capacity
Before: Credit analysts spent 48 hours manually reading documents and keying data into Salesforce.
After: Perfect JSON payloads auto-update Finspectra collateral in seconds.
Net Impact: Eliminated the 48-hour manual "Due Diligence" document review phase, drastically accelerating the sales pipeline, and allowed the firm to scale its $330M private credit book indefinitely without linearly scaling administrative underwriting headcount.