Orbit Analytics

The client's growth team was drowning in data but starving for insight. Their existing BI stack required a data analyst to answer every question — a bottleneck that slowed product decisions by days and created a culture of gut-feel over evidence.

They needed a platform that non-technical users could operate independently, while still giving data engineers the depth to run complex, multi-dimensional analyses on billions of events.

We spent the first three weeks embedded with the client's growth and data teams, shadowing their workflows and cataloguing the questions they asked most frequently. This shaped the entire product — we designed around real questions, not hypothetical use cases.

The technical architecture was designed for time-series data from the ground up. TimescaleDB's hypertable partitioning gave us the query performance we needed at scale, while Kafka handled the ingestion pipeline without data loss under peak load.

For the AI layer, we fine-tuned an anomaly detection model on the client's historical event data, then built a natural language interface powered by OpenAI that lets users ask questions in plain English and receive structured query results.

A full-stack analytics platform with a custom visualization engine built in React — supporting line charts, funnel analysis, cohort retention grids, and heatmaps, all rendered client-side for instant interactivity.

The AI anomaly detection system runs continuously against incoming event streams, surfacing statistically significant deviations with plain-language explanations and suggested next steps. Alerts are delivered via Slack, email, or in-app notifications.

A natural language query interface lets non-technical users describe what they want to know — "show me conversion rate for users who signed up last month by acquisition channel" — and receive a rendered chart in seconds.

Orbit reduced the client's time-to-insight from days to minutes. The growth team now runs 40+ self-serve analyses per week without involving a data analyst, freeing the data team to focus on modelling and infrastructure.

The AI anomaly detection caught a critical drop in mobile checkout conversion 11 hours before it would have been noticed manually — preventing an estimated $180K in lost revenue.