AI Rent Roll Parsing — Without the Hallucinations
AI is great at reading messy rent roll layouts. It's also great at confidently inventing numbers. We use AI for parsing and deterministic math for trust.
3 documents/day free · no signup · documents never retained
The problem with pure-AI parsing
Rent rolls have no standard format. Every property manager's export looks different: merged headers, subtotals mixed with units, charges split across shifting columns, occupancy codes that vary by platform. Multimodal AI handles that variety far better than template-based OCR — but a language model will sometimes fill a gap with a plausible-looking number. On a document that feeds a loan file or a purchase decision, "plausible" is the failure mode.
Parse with AI, verify with math
RentRollAPI runs every document through two stages:
- AI parsing — a multimodal model converts the document (native PDF, Excel, or scan) into one row per unit in a consistent schema: unit, tenant, type, square feet, lease dates, rents, deposit, occupancy.
- Deterministic verification — plain code, no AI, recomputes total rent and unit counts from the parsed rows and reconciles them against the totals printed on the document. Mismatches and low-confidence fields are flagged — never silently guessed.
Proof, not promises
We publish a 100-document public benchmark — real SEC-filed rent rolls, tenant schedules, and T12 operating statements, each linked to its public source. 100% field accuracy (1409/1409 fields), including all 100 documents re-run as simulated 300 DPI scans.
For developers and AI agents
Parsing is available three ways:
- REST API — one HTTP call, verified JSON out. From $49/month.
- .NET SDK — RentRollApi.Client on NuGet.
- MCP server — let Claude, Copilot, or Cursor parse rent rolls as a tool call.
curl -X POST https://rentrollapi.com/v1/extract/rent-roll \ -H "X-Api-Key: rr_live_..." \ -F "file=@rent_roll.pdf"