Built for a Florida industrial value-add investment firm
AI Deal Screening for Commercial Real Estate: 15 Hours to 3 Minutes
We built an AI deal screening system for a Florida industrial value-add investment firm that reads broker emails and offering memos, checks every deal against a 26-point checklist, scores it on weighted criteria, and generates branded investment memos on demand. Underwriting a deal went from 15 hours of analyst work to 3 minutes, roughly 300x faster from intake to memo.
Clients are anonymized by agreement. The system mechanics and first-party numbers below are real.
- 3 min
- to underwrite a deal, down from 15 hours
- 300x
- faster from intake to memo
- 26
- point completeness checklist
- 5 + 3
- weighted criteria plus automatic disqualifiers
Walkthrough
Watch the system run end to end
Demo shown with sample data and a fictional deal.
01 · The problem
The challenge
The firm runs an industrial value-add strategy in Florida: dozens of broker emails per week, every one with an offering memorandum attached. Analysts were spending 15+ hours per deal re-keying property and rent-roll data, running disqualifier checks by hand, building underwriting models, and stitching investment memos together slide by slide. Hot deals slipped because nobody had time to score and memo them in the same week they hit the inbox.
02 · The architecture
The system
Deal capture and AI extraction
Brokers forward an offering memo, flyer, or email to a dedicated capture address. The AI parses the attachment, fills the deal record, and flags missing fields against a 26-point checklist, so no deal enters the pipeline half-documented.
Screening engine with hard disqualifiers
Three automatic disqualifiers (significant vacancy, price above the mandate ceiling, no lease upside) remove dead deals instantly. Five weighted criteria (tenancy, rent upside, location, building size, financial returns) rank everything that survives.
Pipeline and comp database
Kanban and list views with market filters, drag-and-drop stage changes, and an internal comp database with import and export, so the score sits next to the evidence.
Investment memo generator
The analyst uploads the underwriting spreadsheet and the system produces a branded PDF and an editable slide deck from the same extracted data, on demand.
03 · The workflow
How it runs
- Broker email forward
- AI extraction
- Score + disqualifiers
- Hot-deal flag
- Memo PDF + deck
04 · The outcomes
Results
| Dimension | Before | After |
|---|---|---|
| Time to underwrite a deal | 15 hours | 3 minutes |
| Screening | manual checks | 5 weighted criteria + 3 automatic disqualifiers |
| Investment memo output | days of analyst work | branded PDF + editable deck, on demand |
| Deal throughput | 300x faster intake to memo |
Questions
Frequently asked questions
What does AI deal screening actually check?
In this system, every inbound deal is checked against a 26-point completeness checklist, then run through three automatic disqualifiers and five weighted scoring criteria covering tenancy, rent upside, location, building size, and financial returns. Deals that fail a disqualifier are closed with a reason; deals that score well are flagged for same-day attention.
Does the AI make the investment decision?
No. The system screens, scores, and drafts. A human analyst reviews every score and every memo before anything reaches the investment committee. The value is that the analyst starts from a structured, scored deal record instead of a raw PDF.
How does a deal get into the system?
Brokers forward the offering memo, flyer, or email thread to a dedicated capture address. The AI extracts the property, tenancy, and financial fields into a deal record and flags anything missing. Deals can also be entered manually through a form with a live score preview.
Can a system like this work for asset classes other than industrial?
Yes. The screening criteria and disqualifiers are configured per mandate. The same architecture applies to multifamily, retail, or office: the checklist, weights, and disqualifiers change, the pipeline does not.
Related
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Work with us
Want a system like this?
We start with a paid AI audit: we map your workflows, identify where a system like this pays back, and scope the build before any larger commitment. If enablement fits better than software, the AI Team Program trains your team to run AI-native workflows in-house.