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// CASE / 08 / operations

Weather chase platform — multi-source operator console

Internal storm-response team
operationsfield-opsdata-fusionspecialist

Radar + lightning + ADS-B + drone + vehicle telemetry fused into one console so the operator never tabs between tools.

// Cost
Internal build atop existing data fabric.
// Duration
Iterating since early 2026.
// 01 · The problem

An operator chasing a storm was juggling 7+ tabs and apps. Critical decisions (deploy / pull back / re-route) were being made on stale or missed data.

// 02 · What we did

One screen. Radar + EUMETSAT MTG-LI lightning + UDM PTZ feeds + drone telemetry + van GPS + Starlink link health. Each pane is a thin lens over the underlying data fabric (siphon → prism).

// 03 · What the AI did

Lightning-risk scoring (regression on time-to-10km) and vision-based confirmation that a tracked cell looks the way the radar says it does.

// 04 · What humans did

All deploy/abort decisions. The AI surfaces confidence, never authority.

// 05 · The outcome

Operator workload measurably lower. Decision quality unchanged or better. Critically, the system degrades gracefully when feeds drop — operator never gets a blank screen.

// 06 · What broke

First version tried to auto-classify storm severity and present a single recommendation. Operators stopped looking at the underlying data, then got bitten when the model was wrong. We pulled the recommendation; kept the score.

// 07 · What works

AI as scorer and surfacing aid; never as decider for safety-critical work.

// 08 · Reusable lessons
  1. 01If your output drives a decision a human is liable for, the AI must support the decision, not make it.
  2. 02Always show the model's confidence and the underlying signal, not just the output.
  3. 03Field-ops UIs need to assume bad connectivity, not best-case.