Case Studies.

How markets and machines put real-time data to work.

How hedge funds and ML teams put EdgeOrigin's real-time data to work. Detailed, named studies are on the way - book a demo to see the data on your own use case.

Where real-time changes the answer.

Trading signal

A quant desk streams evaluated, real-time signal and ground-truth alternative data - acting on an event before it clears the tape.

Alternative data

A fundamental fund uses satellite and globally sourced observations to form a view ahead of the reported number, with clean provenance.

Model training

An AI team consumes large, fresh, real-world datasets - continuously refreshed and structured - straight into its training pipeline.

The same data, doing two different jobs.

A trading desk and a training pipeline want opposite things from a data provider, and most vendors are built for one of them. A desk wants the earliest possible notice of a single event, evaluated well enough to act on without a human reading it. A model wants enormous, consistently structured volume with a history that does not lie about what was knowable when.

Those requirements only conflict if collection and delivery are the same decision. Because our nodes evaluate and stamp each record as it moves, the live stream and the point-in-time archive are the same records viewed at different distances - so the backtest that convinced a desk to take the position is running on exactly what production will see.

That is the whole argument for buying real-time data from the network that collected it, rather than from a layer sitting on top of somebody else's: the guarantees survive the trip.

How the network makes that possible

What the data is worth in practice.

Act sooner

Signal arrives the moment it happens, timestamped at the edge - so the edge of an event reaches you while it still matters.

Trust the data

Every record carries its provenance and its urgency, authenticity, and data-quality values, so a desk or a model can act on it and trace it back to the observation behind it.

Skip the plumbing

Records arrive clean, deduplicated, and structured - decision-grade on delivery, not another unfiltered dataset your team must parse and cleanse.

What the first thirty days look like.

We would rather be measured than described. An evaluation runs against live coverage on a use case you already have an opinion about, because the only convincing result is one you can check against something you already know.

Pick the events that matter to you

Not our showcase coverage - yours. A commodities desk and an AI infrastructure fund care about different halves of the world, and the evaluation is worthless if it runs on the half you do not trade.

Measure us against what you have

Run our stream alongside your incumbent source and compare on the only two questions that matter: how much earlier the record arrived, and how often it was right. Both are recomputable from the provenance on every record.

Backtest on point-in-time history

Take the same coverage back through the archive and test the signal on what was actually knowable at each moment, with no corrected values leaking backwards into the result.

Move it into production unchanged

The interface, the record shape, and the evaluation values are identical between the sandbox and production, so going live is a credential change rather than a rebuild.

Named studies are on the way. Until then the honest version is this page plus an evaluation on your own coverage - book a demo and we will run it against something you already have a view on. Pricing for each stage is on the pricing page.

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.

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