One edge network, working in both directions.

Edge nodes listen where events happen. The same network delivers from the point of presence nearest you.

Most data vendors run collection in a handful of cloud regions and hand delivery to somebody else. EdgeOrigin operates a single edge network end to end, so the distance an event travels is short on the way in and short on the way out - and every hop belongs to us.

Listening at the edge

Our nodes sit close to where events actually surface, holding open connections to the sources that matter in each region. An event is timestamped by the node that hears it, not by a central collector minutes later - which is where most of the latency in this industry quietly hides.

Nodes distributed across every region we cover, not a few cloud regions
Events timestamped at the node that hears them, with that node recorded
Regional redundancy, so one node or one source going quiet changes nothing

Delivering from the edge

Once machine learning has evaluated an event, our edge network serves it from the point of presence physically nearest your systems. A desk in London and a training cluster in Virginia each receive the same record from their own local edge, not from one distant region.

Streaming and REST served from your nearest point of presence
The serving node is stamped on every record, so latency is auditable
Every hop encrypted with hybrid post-quantum key exchange
No single region to fail, throttle, or add a transatlantic hop

Being first is an architecture, not an effort.

Every data provider says it is fast, and almost all of them are describing the same design: collectors in a handful of cloud regions that go out and ask the world for updates on a schedule, a queue behind them, an enrichment stage behind that, and somebody else's network to carry the result the last few thousand kilometers. Each of those is a place where a record waits. Added together they are the reason the industry quotes latency in minutes and calls it real time.

EdgeOrigin is built to remove the waiting rather than to shorten it. Three structural decisions do the work, and none of them can be replicated by tuning an existing pipeline - they change where the machinery physically sits.

Three decisions that cannot be retrofitted.

We are already listening

A pipeline that polls discovers an event, on average, half an interval after it happens - the interval is a queue whether or not it is called one. Our nodes hold their connections open to the sources that matter in each region, so an event is received rather than found. Nothing about our speed depends on how often we remember to ask.

The first hop and the last hop are both short

Latency at this scale is mostly geography and the number of networks a packet crosses. A collector sitting in one cloud region is a continent away from most of the world's events and another continent away from most of the desks that trade them. We put the listening close to where events surface and the delivery close to where our clients run, so the long haul happens inside our own network instead of on the two legs that bracket it.

Evaluation happens in motion

Most platforms collect first and think second: land the raw volume, queue it, enrich it in a batch, then publish. Quality is bought with time. Our machine learning models evaluate a record while it is moving through the network, so urgency, authenticity, and data quality are settled by the time it reaches the delivery edge - not in a job that runs afterwards.

What happens between the event and your systems.

Five stages, all of them inside our own network. The clock starts at the node that hears the event and does not stop until the record is in your process, and every stage adds something that would otherwise be your engineering team's problem.

Received at the edge

The nearest node is already connected to the source, so the event arrives rather than being fetched. It is timestamped by the node that heard it, and that node's identity is attached immediately. This timestamp is the reference clock for everything downstream - including the latency figure we publish back to you.

Structured and resolved

The raw observation becomes a typed record. Entities are resolved to stable identifiers so the same company, vessel, facility, or instrument is the same key every time; units, currencies, and languages are normalized; and the same event reaching us through several independent sources is collapsed into one record with several attestations behind it.

Evaluated in flight

Our machine learning models - trained on an enormous volume of real-time global data, and retrained continuously against what the world did next - judge three things: how urgent the event is, whether it is authentic and the source is who it claims to be, and whether the record is complete enough to act on. Each is a value on the record, not a filter applied on your behalf, so you decide the threshold.

Sealed with its provenance

Lineage is attached and signed: which node heard it, when, through what class of source, what corroborated it, and what the models concluded. Signing is the point - a record that reaches a compliance review with an unverifiable history is a record the desk cannot defend, however fast it arrived.

Delivered from your nearest edge

Our edge network serves the record from the point of presence physically closest to your systems, over a session negotiated with hybrid post-quantum key exchange. A desk in London and a training cluster in Virginia are each served locally, from the same network, with the serving node stamped on what they receive.

No stage is subcontracted. That is the reason the sequence can be described this precisely at all - a provider that rents any part of it can only tell you about the parts it owns.

Every hop belongs to us.

Owning the network end to end is what makes the latency claim a measurement rather than a marketing number. There is no third party between an event happening and the record reaching your systems, and nothing in the path we cannot account for.

It is also what makes the security claim enforceable. A provider that hands delivery to somebody else can encrypt its own legs and hope about the rest; hybrid post-quantum key exchange on every hop is only a guarantee if every hop is yours to configure.

Every record arrives able to explain itself.

A record is not just a payload. It carries the evidence for its own timing, origin, and trustworthiness, because the questions a research desk gets asked afterwards are always about those three things.

See the delivery interface in the docs

observed_at: The instant the edge node heard the event, to the millisecond, against a network-wide reference clock.
heard_by: The node that received it and the region it covers - so the first hop is attributable, not assumed.
served_by: The point of presence that delivered it to you, which makes the end-to-end latency a measurement you can audit rather than a claim we make.
entities: Resolved identifiers rather than raw strings, stable across every record we have ever sent you.
urgency / authenticity / data_quality: The three values our models assigned in flight, exposed as numbers so you set the threshold instead of inheriting ours.
provenance: Signed lineage: source class, corroborating observations, and the evaluation trail, sealed so it cannot be altered between our network and your systems.

What owning both ends actually buys you.

Owning both ends is not a talking point about vertical integration. It is what turns four claims that every vendor makes - fast, accurate, traceable, secure - into properties that can be measured on a single record and checked by somebody who does not trust us yet.

Latency you can measure

Because we own every hop, the time from event to your systems is a number we publish per record rather than an estimate you have to take on faith.

Provenance per node

Each record names the node that heard it and the node that served it, so a research desk can defend a trade and compliance can trace it.

One clock across the network

Every node timestamps against the same disciplined reference, so records from different regions are directly comparable and ordering holds globally. Ordering is what makes a backtest honest.

One contract, one interface

You integrate once. Regional coverage, failover, and delivery are ours to run, not a set of vendors for you to assemble and reconcile.

Evaluated before it ships

Our machine learning models judge urgency, authenticity, and data quality in flight, so the volume that reaches your systems is the volume worth processing.

Post-quantum on every hop

Node to network to you, every connection is encrypted with hybrid post-quantum key exchange, so traffic recorded today is not readable later. See /post-quantum.

What an infrastructure buyer asks next.

What does "edge" mean at both ends of the network?

It is the same network doing two jobs. Inbound, edge nodes sit close to where events surface and listen there, so the first hop is short. Outbound, the same network serves each client from the point of presence nearest their systems, so the last hop is short too. There is no central region in the middle that everything has to pass through and wait in.

How is this different from a vendor that resells someone else's collection?

A reseller inherits the latency, the coverage decisions, and the gaps of whoever collected the data, and can only add formatting on top. We operate the collection, the evaluation, and the delivery ourselves, which is why we can attach a node identity and a measured latency to every record. It is also why a coverage request is a roadmap item for us rather than a conversation with a third party.

How do you prove the latency you publish?

Every record carries the node that heard the event, the timestamp it was heard at, and the point of presence that served it. That is enough for you to reconstruct the interval independently, per record, rather than trusting an aggregate figure in a datasheet. We would rather be audited than believed.

What happens when a node or a source goes quiet?

Coverage in a region is deliberately redundant, so no single node or single source is the only path to an event. Failure is a regional degradation that the network routes around, not an outage in your data, and the provenance on the records that arrive tells you which path they took.

Do I need infrastructure in a particular region to get the low latency?

No. Delivery is served from the point of presence closest to wherever your systems already run, so you get the local hop without relocating anything. If you run in several regions, each of them is served from its own nearest edge and receives the identical record.

Is the post-quantum encryption on the whole path or just the last leg?

The whole path. Node to network, between our own regions, and network to you - every leg negotiates a hybrid post-quantum key exchange, which is only enforceable because no leg belongs to somebody else. The standards involved are named on the post-quantum security 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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