To maintain a strict 48-hour SLA on $150M enterprise-value loans, financial analysts were manually extracting Annualized Recurring Revenue (ARR) metrics, complex deferred revenue schedules, and cohort churn reports from massive Excel spreadsheets and unstructured PDFs. This manual data entry into proprietary sub-ledgers threatened to cause wildly inaccurate attachment point calculations (exceeding strict <1.25x ARR thresholds), exposing the firm to catastrophic default risk.
ARR Underwriting
& Sub-Ledger Automation
The Bleeding Neck
Manual extraction threatened to cause wildly inaccurate calculations.
Architecture & Prototype
Qeiva Pipeline: Enterprise Value & ARR Extraction
1. Unstructured Data Ingestion (SaaS Financials)
Qeiva engineered a localized, headless Python pipeline utilizing spatial machine learning (LayoutLMv3) to parse complex, unstandardized SaaS financial PDFs and Excel schedules. It extracts ARR data, normalizes it, and pushes it directly into in-house deal-booking applications via API.
The ROI
45 Sec
Extraction Time
100%
SLA Protection
0
Transposition Errors
Before: Data extraction of a complex 100-page SaaS financial package took 14 hours of manual labor.
After: Reduced extraction time to roughly 45 seconds.
Net Impact: Completely eliminated human transposition errors in calculating strict ARR attachment points, protecting the 48-hour SLA.