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// LIVE PRODUCT · CRE UNDERWRITING

DealManagerAI.com

An end-to-end commercial real estate underwriting product. AI-driven rent roll extraction, NOI / cap rate / IRR modeling engine, sponsor-grade reporting layer. Built to give CRE sponsors the same modeling rigor that PE-backed healthcare platforms use on every acquisition.

STATUSLive in production
STACKNext.js · OpenAI · Postgres
USERSActive CRE sponsor
FOCUSRent roll to IRR pipeline
Visit DealManagerAI.com Send me the underwriting model

The problem

// WHY THIS EXISTS

Commercial real estate sponsors spend hours per deal extracting rent rolls from PDFs, building underwriting models from scratch in Excel, and assembling sponsor-grade reporting for LPs. The work is repetitive, error-prone, and inconsistent across deals at the same firm. Most underwriting software is either too rigid (locks you into a vendor's model) or too thin (a calculator with no AI extraction or audit trail).

What the product does

// THREE PIPELINES
01

Rent roll extraction

Upload a rent roll PDF (any sponsor format). The AI pipeline extracts unit-level data, normalizes rent and occupancy across irregular layouts, validates totals, and lands the result in a structured table the underwriter can edit. Confidence scoring on every field. Anything below threshold flagged for review.

StackOpenAI structured outputs · field-level confidence · manual override UI.
02

Underwriting model

NOI build from rent roll plus operating assumptions. Cap rate sensitivity, debt sizing, IRR / equity multiple computation. The model behind it is the same Excel workbook I'd hand a sponsor on Day 1 of an acquisition. Every output has an audit trail back to the source data.

OutputSponsor-grade pro forma, downloadable as Excel or rendered live in-app.
03

Sponsor reporting

LP-ready reporting: deal summary, sensitivity tables, sources & uses, returns waterfall. Generated from the same underlying model so reporting never drifts from underwriting.

OutputOne-click sponsor memo PDF.

Architecture (short version)

// HOW IT'S BUILT

Next.js App Router on the front, Postgres for persistence, OpenAI for extraction with structured outputs and a custom validation layer between LLM output and the database. All underwriting math runs server-side in TypeScript against the same numerical primitives the Excel workbook uses, so the in-app pro forma and the downloadable Excel always reconcile.

What's next

// ROADMAP

Multi-property portfolios. Lender-ready debt sizing module. Comparable-set ingestion from public sources. Eventually a healthcare-platform variant that brings the same pipeline to PE-backed practice acquisitions.

See it live, or take the model.

Open the product. Or pull the underlying Excel underwriting model that drives it.