We combine proprietary socioeconomic and public safety datasets to produce location risk scores at the block group level. No black-box AI — a transparent, auditable algorithm you can explain to a compliance team.
The Problem
How It Works
Source
Federal demographic, economic, and housing datasets at the block level.
Source
Geo-tagged incident records from public safety agencies.
Algorithm
ML model trained on authoritative government socioeconomic and public safety data — identifies which variables predict risk and by how much.
Output
0–10 score per location with confidence intervals.
Model Quality
Why It Holds Up
Tested on data it never saw
Trained on three years of historical data, then validated on a full fourth year before we showed it to anyone. The risk rankings held.
Transparent inputs
Every variable is a named, documented field from authoritative government sources. No proprietary black-box data. You can explain every score to a compliance team.
Stable signal, not noise
The structural factors that correlate with neighborhood risk don't flip year to year. The model captures durable signal — not short-term fluctuation.
How It Works — 4 Steps
Ingest Government Data
Federal, state, and municipal datasets — socioeconomic, housing, and public safety records — at the block group level.
ML Scoring Engine
Machine learning identifies which socioeconomic variables predict risk burden — and by how much.
Score + Predict
Every location gets a 0–10 risk score. The model predicts what police data can't tell you.
API Delivery
REST API, bulk CSV, or webhook. Plug into your existing platform in under an hour.
Data Sources
Currently live in NYC. Expanding nationwide.
Roadmap
Now
ML model trained on authoritative government socioeconomic and public safety data. Block-group level scoring.
→ Q2 2026
Socioeconomic estimates cover all 220,000+ U.S. census block groups.
→ Q3 2026
API key management, usage dashboard, and tiered pricing. Sign up, get a key, start scoring in under an hour.
→ Q4 2026
Layer in real-time data — 311 complaints, Citizen app, permit activity — for dynamic scoring that reflects what's happening now, not last year.
Use Cases
Enrich listings with neighborhood risk context. Give buyers better data without the Fair Housing liability of crime labels.
Power investment scoring, portfolio risk analysis, and automated underwriting with signals that actually predict outcomes.
Replace unreliable crime indexes with a consistent, auditable, and legally defensible risk signal for property underwriting.
Powered by Authoritative Government Data Sources
We're onboarding a limited number of design partners for private beta. Request a live demo with your actual addresses.
We'll walk you through a live API call on your dataset.
No spam. Demo request only. We respond within 1 business day.
