Linking records that refer to the same real-world entity across different sources, spellings, and identifiers.
Entity resolution is the problem of deciding when two records - possibly from different sources, with different spellings, formats, or identifiers - refer to the same underlying thing. It is what lets you connect a company named one way in a news article, another way in a filing, and by a ticker in exchange price data into a single coherent entity.
In practice it combines deterministic matching on shared identifiers with fuzzy matching on names and attributes, and it never fully goes away because the world keeps supplying new aliases, mergers, and renamings. Done well, it is invisible; done poorly, it fragments one company into three or merges two into one, and every downstream count and signal inherits the error. It is closely tied to deduplication, which is entity resolution applied within a single stream.
A live evaluation measures EdgeOrigin against your coverage requirements: decision-ready real-time data, delivery latency into your systems, and record-level provenance.