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Support Center

Everything you need to connect to and consume EdgeOrigin data.

Search the knowledge base or browse by category - getting started, data and coverage, delivery and integration, provenance and trust, and billing. Questions about the interface are answered in the documentation; questions about a word are answered in the glossary; anything specific to your account reaches the team directly.

Getting started

How do I connect to the EdgeOrigin network?

Authenticate with your API key, then pull records from the REST API or subscribe to the live stream. The Quickstart in the docs walks through it end to end.

How long until I get data?

Minutes. Once your access is provisioned you can query history and open a streaming connection right away.

Do I need special infrastructure?

No. Data is delivered over standard HTTP and streaming transports that integrate with existing data pipelines and research environments.

Data & coverage

What sources does EdgeOrigin cover?

Global real-time listening across news and web sources, plus satellite and other alternative sources - normalized, deduplicated, and evaluated before delivery.

How fresh is the data?

Real-time. Records are timestamped at the edge and streamed as they are observed, with minimal added latency between event and delivery.

Is historical data available for backtesting?

Yes. Point-in-time history is available alongside the live stream through the same interface, so what you test on matches what you trade or train on.

How is each record evaluated?

Our machine learning models judge every record for urgency, authenticity, and data quality, so you can consume everything or just the high-confidence tail.

Delivery & integration

How is the data delivered?

Two ways: a streaming API that pushes records as they arrive, and a bulk/REST API for history and batch pulls. Both are available on every tier.

What formats do you support?

Structured JSON over the API and streaming transports, with provenance fields on every record.

Can you deliver to our data warehouse?

Yes - talk to us about direct delivery into your data plane. Enterprise plans include custom delivery options.

Provenance & trust

Can I trace where a record came from?

Yes. Every record carries provenance - its source and collection timestamp - so you can trace a value back to the observation behind it.

How do you handle data quality?

The pipeline deduplicates records and evaluates urgency, authenticity, and data quality in flight. Freshness and quality are monitored continuously so regressions are caught before delivery.

Is the data point-in-time accurate?

Yes. History preserves what was known at each timestamp, so backtests don't accidentally use information that wasn't available at the time.

Billing & plans

How is EdgeOrigin priced?

By the coverage you take and how you consume it - streaming, bulk history, or both - with enterprise terms for high-volume desks and training runs. See the Pricing page or contact sales.

Can I evaluate it first?

Yes - book a demo and we'll set you up to evaluate the data on your own use case.

How do I get an invoice or update billing details?

Account administrators can manage billing from account settings; for changes, contact our support team.

Answered by the people who run the network.

Support here is not a tier of the product. Every account reaches engineers who operate the edge network rather than a script-reading layer in front of them, because most questions about a real-time dataset are questions about the network that produced it: why a record arrived when it did, why a region looks quiet, why two sources disagree about the same event.

That shape is possible because the answer is usually already in the data. Every record names the edge node that heard it, the point of presence that served it, and the provenance behind it, so a question about timing is a subtraction rather than an investigation. When you send us a record identifier, we are reading the same evidence you are.

What we ask in return is specificity. A stream name, a record identifier, and the window you were looking at turn a support thread into a measurement; "the data seemed slow yesterday" turns it into a conversation. The documentation covers the fields that carry the evidence, and the quickstart shows where to find them in a response.

Four questions, four places.

Most of what teams need is already written down. This is where each kind of question is answered fastest, in the order we would try them.

How the product works

The documentation covers authentication, the record shape, and the choice between streaming and bulk history. Start there for anything about the interface.

What a term means

The glossary defines the vocabulary this industry uses loosely: latency, point-in-time history, delivery guarantees, and the rest.

Why the data behaved that way

Questions about timing, coverage, or a quiet region are usually architecture questions. The edge network page explains what each stage guarantees.

Anything specific to your account

Coverage requests, commercial terms, and anything with a record identifier attached go to the team directly through contact.

What integration actually involves.

The integration is deliberately small. You authenticate with an API key, pull a page of history from the REST endpoint to see the record shape, then open a streaming connection for anything whose value decays in seconds. Both read the same records through the same authentication, so nothing you build against one is thrown away when you adopt the other.

The design work that matters happens on your side of the boundary, and it is the same for every real-time source: make your consumer idempotent so a retried record is not counted twice, key your storage on the record identifier, and decide how long you will wait for late arrivals before closing a window. Teams that skip these do not see an error - they see totals that drift, which is the expensive kind of bug.

Moving from an evaluation to production is a credential change rather than a rebuild. The interface, the record shape, and the urgency, authenticity, and data quality values are identical in both, which is the same property that lets a backtest predict what production will see.

Didn't find the solution?

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