Data Platform

Real-Time Analytics Engine

Live KPI dashboards with anomaly detection and drill-down analysis for a multi-store retail operation.

Next.jsPostgreSQLD3.jsPythonKafka

At a glance

What this shipped

The numbers that mattered to the client — measured before and after.

Real-time
Data freshness
Automated
Anomaly alerts
SKU-level
Drill-down
Auto-generated
Reports

The problem

What we were called in to fix

A retail operator ran on overnight batch reports — by the time a problem showed up in a spreadsheet it was a day old and the money was already lost.

Data lived in half a dozen systems with no single, trustworthy view, and no one could drill from a top-line number down to the cause.

Our approach

How we actually built it

No magic — just the right architectural calls in the right order.

We built streaming pipelines that ingest sales, inventory and ops events in near-real-time into one queryable store.

Live dashboards surface the KPIs that matter, with anomaly detection that flags unusual dips and spikes automatically instead of waiting for someone to notice.

Every chart drills down — company to store to SKU to transaction — so the 'why' is one click from the 'what'.

Automated daily summaries land in inboxes without anyone building a report by hand.

The outcome

What changed for the client

Decisions now run on live data instead of yesterday's batch.

Automated anomaly alerts catch issues within minutes, not the next morning.

One source of truth replaced a tangle of conflicting spreadsheets.

Tech stack

Every meaningful piece

Next.jsTypeScriptPostgreSQLKafkaPythonD3.jsClickHouseRedis

We don't do generic case-study writeups. Want the unredacted version with names, screenshots and architecture diagrams? We share those on a call.

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