Speech-to-text technology has gotten remarkably good. But every organization's calls are packed with company names, product terminology, agent names, and industry jargon that generic transcription models weren't trained on. When a transcript gets those wrong, every analysis built on top of it inherits the error.
Contextual Entity Resolution (CER) fixes that. It's an intelligent layer within Compass's transcription processing that uses each client's business context to automatically correct errors before scoring, coaching, or analytics run.
Company and product names. Proper nouns that speech-to-text models stumble on get matched against a learned profile of each client's terminology.
Agent identification. Names get phonetically distorted in transcription constantly. CER matches distortions against the client's actual agent roster, so every call is attributed to the right person without manual tagging.
Industry terminology. Medical billing codes, financial product names, equipment part numbers. Every domain has terms a general-purpose model will get close but not right.
Contextual corrections. A client's location or brand name rendered as a similar-sounding common phrase. These require understanding the client's world, not just general language patterns.
CER starts learning after just a few calls. No manual dictionaries. No onboarding forms. The system adapts continuously as it processes more interactions.
Compass is built on a truth-first principle. Every evaluation and insight is only as reliable as the transcript underneath it. CER ensures that foundation is solid.
CER is live for all Compass clients and runs automatically on every interaction.
