Identifying and tagging the real-world entities in text - companies, tickers, people, places - so unstructured content becomes linkable.
Named entity recognition finds the entities mentioned in a piece of text and labels what they are: this token is a company, that one a person, this a location or a monetary amount. It is the step that turns a wall of prose into something you can filter and join - without it, an article is just words, and with it, it becomes an event attached to specific companies.
The genuinely hard part in a finance context is not spotting that a name is a company but mapping it to the right identifier: "Apple" the fruit versus the company, "Delta" the airline versus the variant, a subsidiary versus its parent. That disambiguation hands off to entity resolution, and getting it wrong quietly attaches a signal to the wrong security. Ticker and identifier mapping is where a lot of the real engineering effort goes.
A live evaluation measures EdgeOrigin against your coverage requirements: decision-ready real-time data, delivery latency into your systems, and record-level provenance.