The real-time data layer for markets and machines
The events that move markets and the data that trains models happen in the real world first. Why a real-time layer is becoming its own category.
Every interesting signal starts as an event in the physical or digital world: a fabrication plant changes utilization, a grid interconnect filing appears, a satellite passes overhead, or a launch window opens. By the time that event reaches a clean, packaged vendor dataset, most of its value has already been priced in or has gone stale for training. The gap between when something happens and when you can act on it is where the edge lives.
Closing that gap is an infrastructure problem, not a dashboard. It means edge nodes listening to real-world events worldwide, normalizing and timestamping each observation at the receiving node, evaluating the raw stream into signal rather than passing through unfiltered data, and delivering it - by stream or in bulk - the moment it happens. Each of those steps is hard on its own; doing all of them together, reliably, at low latency, is the product.
We think about it as one layer with two customers. Hedge funds want real-time, ground-truth signal to trade on. AI teams want large, fresh, high-signal training data to build on. The same listening and evaluation pipeline serves both - markets and machines - which is why we build it as shared infrastructure rather than two separate products.
The rest of this blog will go deeper on the pieces: global event listening, satellite and alternative data, the in-house models that evaluate every record, and how we keep provenance attached to every record so you can trust what you act on.
Further reading