WatchLock —
AI-powered cargo theft prevention
A 2013 IoT fleet security product — hardware digital locks on liquid cargo tankers with GPS and geofencing — redesigned for 2026 with an AI intelligence layer that shifts fleet managers from reactive firefighters to proactive supervisors.
ROLE
Lead UX · End-to-end
PLATFORM
iOS + Android
DURATION
8 months
STATUS
Shipped
✦ Predictive halt scoring
✦ CV tamper detection
✦ Explainable AI
✦ Volume forecasting
GPS + geofencing
Multi-drop trips
92%
Parent alert accuracy after AI tuning
3x
Faster incident response vs manual review
0→1
Full product designed and shipped
01 — THE PROBLEM
Parents worry about their teenage drivers but existing apps either overwhelm with raw GPS and data or send alerts too late. The challenge: how do you give families real safety insight without making the app feel like surveillance.
02 — PROCESS
1
Discovery
12 parent + teen interviews. Mapped anxieties and trust gaps.
2
Define
3 personas, journey maps, core use cases prioritised.
3
Design
Lo-fi → hi-fi. 4 concept directions, 2 rounds of usability testing.
4
AI layer
Designed risk score UI, explainability pattern, alert logic.
5
Ship
Handoff, design QA, and post-launch iteration.
03 — AI FEATURES DESIGNED
AI DESIGN DECISIONS
These weren’t backlogged features — each AI capability was designed with a specific user anxiety in mind and paired with an explainability pattern so users could trust the output.
Trip risk scoring
ML model scoring each trip 0–100 on speed, braking, distraction signals. Designed the score card, trend chart, and “why this score” explainability drawer.
Driving behaviour forecast
Predicted weekly behaviour drift based on time-of-day patterns. Designed the weekly digest card and proactive nudge notification system.
Autonomous incident playbook
When risk threshold triggers: auto-notifies parent, suggests a check-in call, logs the incident. Designed the escalation flow and override controls.
Explainable alert UI
“Hard braking detected near school zone at 7:42pm” — not just “bad trip.” Designed the alert anatomy to always show the signal, not just the verdict.
04 — KEY SCREENS
Home / garage
AI risk history
Trip history
Alert explainer
Family members
05 — WHAT I LEARNED
The hardest UX problem wasn’t the AI — it was trust. Parents needed to understand *why* an alert fired before they would act on it. Designing explainability as a first-class surface (not a footnote) was the decision that most improved usability test scores. The AI is only as good as the UI that frames it.
NEXT PROJECT
AI-Triaging
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