Demo

Turning the world's raw signal into decision-grade data.

EdgeOrigin is a New York data company building the real-time layer for markets and machines. We run our own global edge network: events are heard by the edge node nearest them, evaluated in flight by our machine learning models for urgency, authenticity, and data quality, and delivered from the edge nearest the desks and AI teams that act on them.

NY
Headquartered
Real-time
Delivery
Global
Coverage
2
Buyers: markets & machines

A network, not a data catalog.

Most companies that sell real-time data are software companies with a licensing department. They buy collection from someone, normalize it, and compete on the interface. It is a reasonable business, and it puts a hard floor under how early their customers can possibly know anything - you cannot be faster than the source you are reselling.

We started at the other end and built the collection layer first: our own edge nodes, positioned close to where events surface, holding their connections open rather than polling on a schedule. On top of that sit machine learning models trained on an enormous volume of real-time global data, which evaluate every event in flight for urgency, authenticity, and data quality. Delivery is our own edge network too, serving each client from the point of presence nearest their systems over post-quantum encrypted transport.

Owning all three is expensive and slow to build, and it is the only configuration in which the latency figure, the provenance, and the encryption guarantee are things we can actually promise rather than things we inherit.

The AI era runs on data that is fresh, or it does not run.

Two things changed at once. Models became the main consumer of large-scale real-world data, and the events that move capital moved into infrastructure - compute, chips, power, minerals, orbit, and the networks between them. The vocabulary of a market-moving event stopped being quarterly and started being physical, which is a much harder collection problem and a much more valuable one.

Both audiences want the same underlying thing: what happened, verified, structured, and early. A desk wants it because information priced in is information that pays nobody. A training team wants it because a model grounded in a stale corpus is confidently describing a world that has moved on.

That is the entire premise of the company. Everything else - the node placement, the evaluation models, the post-quantum transport, the provenance on every record - is what it takes to serve those two audiences from one network without compromising either.

What we believe.

We come from data infrastructure, quantitative research, and machine learning - applying that discipline to listening, evaluation, and delivery across the real-time network.

01

Ground truth first

Our edge nodes listen to real-world events where they occur, in real time - not to descriptions assembled after the fact.

02

Signal over unfiltered data

Raw data is mostly noise. Our job is to deliver the part that carries information, with its urgency, authenticity, and data quality already evaluated.

03

Provenance by default

Every record carries where it came from and when. If you can't trace it, you can't trust it.

04

Latency is a feature

The value of an observation decays fast. We treat the delay between event and delivery as the thing to minimize.

Real-time data, at edge speed.

A live evaluation measures EdgeOrigin against your coverage requirements: decision-ready real-time data, delivery latency into your systems, and record-level provenance.