Not all AI is created equal: The case for customer service-specific AI

Most AI agents running in contact centers today are built on general-purpose models that come with defaults designed for long-form text and screen-based interactions. On a live customer call, that means responses that are longer than they need to be, phrased for a reader rather than a listener, and delivered at a pace that leaves callers wondering whether their request was heard.

At enterprise scale, those defaults become a commercial problem too. The cost and latency of running millions of conversations through third-party models are difficult to sustain.

Raven is PolyAI's answer to both. A model built specifically for customer service and trained on millions of real conversations. In this session, Matt Henderson, VP of Research, walks through why we train in-house, how we do it, and what it delivers on a live call.

You'll leave with a clearer picture of:

• Why general-purpose models come with the wrong defaults for customer service, and what building specifically for the job changes
• Why latency and cost at enterprise scale make third-party models difficult to sustain, and how a purpose-built model addresses both
• What training on millions of real customer service conversations changes about default behavior
• How PolyAI measures performance and what that means for your customers