To deliver on their advertised "same day turnaround" SLA for urgent bridging finance, credit analysts were forced into exhaustive "stare-and-compare" data entry. Highly compensated analysts manually extracted biographic data, asset valuations, and liability metrics from unstandardized SME bank statements, ATO tax returns, and fillable PDFs. This manual ingestion bottleneck jeopardized their SLAs and artificially capped the firm's revenue velocity.
Automated Data Ingestion
for Private Credit
The Bleeding Neck
Exhaustive "stare-and-compare" data entry.
Architecture & Prototype
Qeiva Infrastructure: SME Bridging & PDF Extraction
1. Inbound Broker Submission (Unstructured)
Qeiva deployed a custom Python OCR pipeline that deterministically extracts text from complex PDFs. The system instantly rips critical financial data, maps it into clean JSON formats, and pushes it directly into the internal reporting system or LOS via a secure API.
The ROI
3 Sec
Extraction Time
$90k+
Salary Overhead Saved
100%
Compliance Integrity
Before: Extraction and data entry took up to 45 minutes per file manually.
After: Reduced extraction and data entry time down to 3 seconds per file.
Net Impact: Eliminated LLM hallucination risk, guaranteed regulatory data integrity, and saved a minimum of $90,000+ in base salary per analyst.