AI deal intelligence for real estate investment

Ingests listings, predicts ROI and rental yield, scores risk, and generates standardised investment memos in seconds — replacing weeks of manual analyst work per deal.

Before. Each deal cost about a week of analyst work. Templates and assumptions differed between analysts, so memos were late and argued over, and the best opportunities were gone by the time the paperwork agreed with itself.

What we built. A custom AI platform that ingests listings, predicts ROI and rental yield, scores risk, and generates a standardised investment memo — with live models the partners can adjust assumptions on, plus continuous alerts on listings, comparables and macro shifts.

Outcome. Research time cut 95% and investment accuracy up 80%. Every memo comes from the same model, so the team argues about the numbers rather than whose spreadsheet is right — and reviews roughly three times as many deals. Related: AI for real estate and AI for financial services.

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Frequently Asked Questions

1. What exactly does your AI agent do?

In this real estate build, Genium's AI platform ingests property listings, predicts ROI and rental yield, scores deal risk, and generates a standardised investment memo in seconds. It also runs continuous alerts on listings, comparables, and macro shifts, and partners can adjust the underlying model assumptions directly rather than working from a static one-off report.

2. How do you ensure the AI works in production, at scale?

For this build, production reliability comes from a single live model producing one standardised memo format that every deal runs through, rather than analysts using different templates and assumptions. The proof is in the outcome: research time per deal dropped 95% and the team now reviews roughly three times as many deals without adding headcount.

3. How will your solution protect our data (and our customers' data)?

Because this platform was custom-built rather than bought off the shelf, data handling — access controls, storage location, retention — is architected around the client's own infrastructure and requirements rather than a generic vendor policy. Exact protections (encryption standards, permission tiers, audit logging) are scoped during the build to match the sensitivity of listing and deal data involved.

5. How does your solution keep a human in the loop?

Partners in this build keep control by adjusting model assumptions directly on the live investment models behind each memo, rather than accepting the AI's output as a final call. The AI handles ingestion, ROI/yield prediction and risk scoring; the investment decision and any assumption overrides stay with the analysts and partners reviewing the memo.

6. How will we know what the AI is doing — and why?

Every deal produces a standardised investment memo showing the ROI/yield prediction, risk score, and assumptions used to reach it, so the reasoning is visible rather than buried in an individual analyst's spreadsheet. Because all memos come from the same underlying model, disagreements become a debate about the numbers themselves rather than about whose calculation method to trust.

Chatbot or Agent?

This real estate deal intelligence build is an agent, not a chatbot: it ingests listings, predicts ROI and yield, scores risk, and generates a full investment memo on its own rather than answering questions in a conversation. Where a chatbot would field queries about a deal, this system produces the finished deliverable directly, cutting roughly a week of manual analyst work per deal by 95%.