Which properties in your target ZIP codes carry the highest compound climate risk?

Watch us score thousands of residential properties for wildfire, flood, and building risk in a single live session, using natural language queries against real datasets, all from VS Code.

What this session covers

We’ll tackle one of the most time-consuming questions in underwriting: compound property risk at scale. Using the Wherobots MCP in VS Code, we go from a natural language question to a weighted risk model, a scheduled monthly pipeline, and a live risk heatmap, without leaving the editor.

The datasets are already in the Wherobots catalog: 2M+ wildfire records, Regrid parcels with FEMA flood zones and building valuations, and Overture buildings with roof material and height. No data wrangling setup. No waiting.

Agenda
  • 5 min — Context and business question setup
  • 25 min — Live MCP demo: natural language queries, catalog exploration, weighted risk model build (wildfire proximity 40%, flood zone 30%, building age 20%, roof material 10%)
  • 5 min — Scheduling the model as a monthly production job
  • 8 min — Vibe-coding a risk heatmap: properties colored green to red by score, with clickable popups showing component breakdowns
  • 10 min — Q&A
What you will walk away with

A clear picture of how to score compound property risk across large portfolios using spatial data that is already structured and ready to query. The same model runs as a scheduled job, so your risk scores stay current as new wildfire and flood data comes in.

Bonus for attendees

Sign up for a Pro trial within 7 days of attending and get a free 30-minute 1-on-1 session with a Wherobots spatial data engineer to adapt this demo to your data.

Who should attend

Underwriting teams, climate risk analysts, reinsurance modelers, and insurtech engineers who need property-level risk scores at scale without building the data infrastructure from scratch.