AI-Native Real Estate Fund
4 AI agents handle deal scouting, underwriting, outreach, and structuring across distressed properties and land parcels.
// Deal pipeline — scout, underwrite, outreach and structure
Figures from the engagement, shared under NDA.
We publish them unchanged and can’t break them down any further here. The reference build you can open runs entirely on sample data.
What it does — and which part of it we built.
4 AI agents powering a Scout → Underwrite → Outreach → Deal Structure pipeline. 13-rule distress detection engine.
XGBoost ML scoring (0-100%). An LLM underwriting agent generates Buy/Pass/Watch briefs. 3D Mapbox with Street View popups.
Every screen here is from the reference build, not the client’s system.
The captions say what you are looking at. Every name and record on them is sample data.
The stack was picked per constraint, not per house habit.
6 technologies across 3 layers — the shape of the stack follows the problem.
Every feature in the build (6)
- 3 parallel data providers: ATTOM (8 API endpoints), RESO MLS (OData client), Probate (signal-based detection)
- 13-rule distress classification across 2 tiers
- XGBoost on Flask for distress scoring (11 features)
- LLM agents for underwriting briefs and deal structuring from 9-strategy library
- 3D Mapbox with Street View popups
- Subscription tier gating across SFR, multifamily, commercial, and land
Don’t take our word for it — it is running in the frame below.
This is the same page the provenance block links to: our reference build for AI-Native Real Estate Fund, with its own look, copy and motion. Scroll inside it, or open it full-screen.
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