AI in your world
The specific, high-value problems AI Data Infrastructure solves — industry by industry. Pick any one to see the full picture: the situation, why it matters, how we’d deliver it, who it’s for and when it bites.
industries
use cases
Manufacturing · 11 use cases
the stakes — $50B/yr lost to unplanned downtime (Deloitte); AI-in-manufacturing ~35% CAGR to 2030 (Grand View).
High-throughput sensor & PLC telemetry pipelines
Land millions of tags a second from PLCs, historians and edge devices into the lakehouse without dropping a reading.
the situation
Plant-floor machines emit readings far faster than an overnight batch load can absorb, so decisions run on data that is hours old by the time it lands.
why it matters
Edge-model decisions are only as fresh as the stream feeding them — and OT data arrives faster than any nightly batch can hold.
how we’d deliver it
We build streaming pipelines that carry every machine reading continuously into one central store (a 'lakehouse' — a single place that holds raw and analysis-ready data together), with buffering so nothing is lost under load.
who it’s for
Plant and operations leaders sponsor it; process engineers and analytics teams live off the resulting live data.
when it bites
When real-time dashboards or edge decisions are wrong because the feed can't keep up with how fast the floor produces data.
evidence Poor data quality costs organisations ~$12.9M/yr on average (Gartner)