Atlas
An analyst triage tool for AI-detected objects in overhead imagery — built for the moment the model is good enough and the human becomes the bottleneck.
Scoped and shipped solo in 5 days · PRD → build → deploy → recorded walkthrough
The analyst is the bottleneck.
Atlas is an interface for an intelligence analyst to review, accept or reject, and dispatch AI-generated object detections on satellite or drone imagery. It optimises for time-to-dispatch under accuracy constraints — once the model is good enough, the product lives in how fast verification can happen without collapsing trust.
This is not a real defense product. It's a 5-day prototype built to demonstrate product thinking, AI-native shipping, and taste in workflow design — and it's honest about that scope: no trained custom model (YOLOv8 pretrained, run offline), no auth, no real-time ingest, and the persona is desk-research rather than user-research. v2 starts with real analysts.
Three trade-offs that mattered most.
Pre-select high-confidence detections as confirmed
Saves clicks on the easy 70% — but risks trust collapse if the model is wrong on something the UI presented as confident. Mitigated by logging every override.
Colour-code confidence; don't just show numbers
Pre-attentive processing is faster than reading under time pressure. Numbers stay available on hover for analysts who want them.
One-button dispatch, no confirmation modal
Every confirmed detection has already been reviewed individually. Adding a modal adds friction without adding information.
What I deliberately didn't build.
Each of these would have been fast to build — and would have weakened the product:
- A select-all button
- Auto-dispatch above a confidence threshold
- A confidence-tuning slider in the main UI
The metrics I'd instrument.
What I'd build next.
- Five real user interviews with analysts before any v2 spec
- Override-driven model retraining loop
- Multi-analyst handoff for flagged detections
- Real-time ingest pipeline