AI tailored for
Private Credit
Automate financial spreading, loan sizing, and credit memo prep so your analysts spend time on judgment, not data entry.
From property-level documents to investment-committee ready model
Spread Financials in Minutes
Upload an OM or borrower package and populate your sizing model automatically.
- Extract rent rolls, T-12s, and key terms from any PDF
- Every cell linked to its source page and line item
Conversational Underwriting
Iterate on assumptions in natural language, not cell references.
- Ask 'what if exit cap is 6.5%?' and watch the model recalculate
- Stress-test DSCR and LTV across rate and vacancy scenarios
Quantify the cost of manual underwriting
Hours per week each analyst spends spreading financials from borrower docs into Excel.
Your team is losing ~1,091 hours/month to manual spreading. That's $43,646/month in analyst time.
Parcella reclaims 80% of that -- freeing ~$34,917/month for analysis and deal evaluation.
Industries
Built for lending teams.
Across every deal type.
Spread borrower financials into your sizing model in minutes, not hours. Automate rent roll and T-12 extraction so analysts focus on credit judgment and Guide compliance, not data entry.
Acquisitions teams receive dozens of OMs a week with compressed bid timelines. Parcella extracts property financials, populates your pro forma, and flags deals that miss your return threshold -- so analysts spend time on IC prep and site visits instead of re-keying rent rolls.
Balance sheet lenders need to size quickly without losing fidelity on property-level performance. Parcella pulls operating statements, rent rolls, and loan structure details into your model so teams can move from documents to committee-ready credit views faster.
Move at the speed your borrowers expect. Automate financial spreading and stress testing across rate and occupancy scenarios so you can quote same-day without sacrificing diligence.
Testimonials
Real results from
real lending teams.
“Our analysts were spending 3 hours per deal pulling numbers from OMs into Excel. Now they upload the PDF, select our template, and the model is populated in minutes. We've cut our time-to-quote in half and the accuracy is better than what we were getting manually.”
Anthony Feyock
CFO
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