Analytics that answers questions.
A self-serve analytics platform for e-commerce sellers — interactive dashboards at SKU and city granularity, with an AI insight layer that says what the charts mean.
01Problem
Sellers were exporting spreadsheets to answer basic questions: which SKU is growing, which city is slipping, what changed this week. Analysts became a bottleneck; decisions ran days behind the data. The existing tools rendered charts — they didn't answer questions.
02Design
The organizing principle: every screen must answer a question a seller actually asks, in their words, in under three seconds. Dashboards were designed backwards from those questions. Filters compose instead of stacking; drill-downs preserve context; and an insight strip narrates each view — "West-zone prepaid orders up 23%, driven by two SKUs" — so the reading is done for you.
03Architecture
04Engineering
- Pre-aggregation over cleverness. The heaviest questions were known in advance, so the pipeline materializes SKU × city × day rollups — the UI never waits on a raw scan.
- Perceived performance. Skeleton-free rendering: charts draw from cache immediately, then reconcile. No spinners on the money screens.
- Virtualized everything. Ten-thousand-row tables filter and sort at 60fps because only what's visible exists.
- Insight guardrails. The LLM narrates only computed aggregates — it never invents numbers; every claim links to the chart that proves it.
05Impact
06Lessons
Enterprise users don't want more data — they want fewer decisions. The AI layer succeeded precisely because it was constrained: narration, never invention. Trust, once earned, made everything else adopted.