Overview
Pipelines that hold up at volume and under time pressure: petabyte-scale log ingestion, vehicle telemetry captured off the CAN bus, and real-time dashboards over data that is still arriving. Instrumentation first — a model can only reason over data that was captured properly.
In practice
- High-volume ingestion and near-real-time processing
- Telemetry capture from hardware and vehicles
- Observability with Grafana, Prometheus and the ELK stack
Where this shows up
- The telemetry pipeline behind Apex Insights: capture off the car, processed and returned to the pitwall while the session is still running. Ours, and the latency budget is ours to solve.
- Vehicle telemetry for the ARC4, integrated end to end and feeding real-time AI coaching at a race school.
- Observability across five live properties — Grafana and Prometheus, with analyzer capacity auto-scaling on live session load.
Behind it: the founder’s background
Oz’s own career rather than the company’s work — running since 1997 and still going. It is here because it is why we can do the above.
- Petabyte-scale log analysis with detections running in near real time. Volume is what makes this hard: at that scale an inefficient pipeline is not slow, it is impossible.
- The log pipeline underneath it — Windows Event Forwarding, syslog collection, and network and firewall telemetry.
- A real-time carbon footprint dashboard covering an entire Fortune 500 games company and its players across every platform — hundreds of millions of sessions resolved into a current number rather than a quarterly report. It won the company’s internal innovation award for project impact in 2022.
- CAN bus data capture from road and race cars during audio recording sessions for Need for Speed — instrumenting vehicles for data in 2012, which is the same job as the ARC4 telemetry stack today.
Let’s talk
If the data exists but you can’t use it — too much of it, arriving too fast, or never captured cleanly in the first place — that’s the problem we like most. Tell us what you’re trying to see.
Talk through the data