How we built CrimeLens: AI over real-time geospatial data in production

A technical deep-dive into building real-time crime intelligence for Canadian real estate.

CrimeLens is a real-time AI neighbourhood intelligence platform, live across 12 Canadian cities. Real estate professionals use it to give clients objective, data-driven neighbourhood risk assessments.

The problem

Real estate agents had no reliable, real-time source of neighbourhood crime and safety data. The data existed — in police open data feeds — but it was raw, inconsistent across municipalities, and required significant processing to be useful.

What we built

We built a geospatial data ingestion pipeline that pulls from multiple Canadian police open data feeds, normalizes the data, and feeds it into a PostGIS database. On top of that, we built ML models that score neighbourhood risk across multiple dimensions and trend those scores over time.

The front end is a web application that lets agents and buyers search any Canadian address and see a detailed breakdown of crime trends, risk scores, and neighbourhood context.

The hard parts

Every municipality publishes their data differently. Some use GeoJSON, some CSV, some have APIs, some are static file dumps. We built a normalization layer that handles the inconsistencies and outputs a consistent schema regardless of source.

The other challenge was making the risk scores meaningful rather than just raw counts. A neighbourhood with 100 incidents in a dense urban core is very different from one with 100 incidents in a suburban area. We built population-density weighting into the scoring model to make comparisons meaningful.

The outcome

CrimeLens is live across 12 Canadian cities, processing real-time data feeds. It's used daily by real estate professionals to give clients objective neighbourhood context.

If you're building a geospatial AI product, book a strategy call. We know this problem well.