Analyzing and scoring voice agent calls with the LLM Gateway

Analyzing and scoring voice agent calls at scale can be efficiently achieved by transforming each call into structured, scored data using transcription and an LLM (Large Language Model) for summarization and evaluation, as facilitated by AssemblyAI's LLM Gateway. Accurate transcription is crucial, as errors can lead to incorrect scoring, and Universal-3 Pro with speaker diarization helps ensure precise, speaker-attributed transcripts. The LLM Gateway, which integrates with models like OpenAI's GPT, Anthropic's Claude, and Google's Gemini, allows for customizable summaries and scoring against predefined criteria such as task success, resolution without escalation, customer sentiment trajectory, and policy adherence. This hybrid approach, combining deterministic Speech Understanding features for objective signals with LLMs for evaluative tasks, ensures consistent and cost-effective scoring, enabling teams to identify and address failure patterns for continuous improvement of the voice agent's performance.

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