AI Analytics Dashboard

The problem

The team's numbers lived in five places and agreed with each other in none of them. Every Monday, an analyst spent most of a day stitching exports together into a slide deck that was already stale by the time it was presented.

Because reporting was expensive, it happened rarely. And because it happened rarely, decisions got made on gut feel between reports — which is exactly the window where the expensive mistakes happen.

What we built

We started by watching how the weekly report was actually assembled, then built the pipeline that made it unnecessary:

• Real-time ingestion from every existing source, normalised into one schema
• Role-based views so a founder, a marketer, and an ops lead each land on the numbers they own
• Predictive flags that call out trends before they show up as a bad month
• Self-serve filtering and export, so the analyst stopped being a bottleneck

The interface was deliberately boring: fast, legible, and consistent. Nobody needs a training session to read it.

The outcome

• Decisions that used to wait for Monday now happen the same day
• The weekly reporting ritual disappeared entirely — roughly 15 hours a week back
• The pipeline handles 10M+ events a day without the team thinking about it
• Data disputes between departments stopped, because there is now one number

The dashboard stopped being something we look at once a week. It's the first tab open every morning.