Glossary · AI & Agents
Call Transcript Evaluation
Overview
What it is
Call transcript evaluation (a form of speech analytics) is the process of judging the quality of AI phone-agent calls by analyzing their transcripts. Instead of listening to every call, teams score a sample or the full set against a rubric: did the agent answer correctly, complete the task, follow policy, and sound natural?
It is the quality-control layer of a voice-AI system — the equivalent of QA for a human call center, but automated and continuous.
How it works
Calls are transcribed via speech-to-text, then each transcript is graded on criteria like task success, factual accuracy, latency, interruptions, and sentiment. Grading can be manual (a reviewer with a scorecard) or automated with an LLM-as-a-judge that applies the same rubric at scale.
Tools such as Coval, Hamming AI and Langfuse specialize in evaluating and monitoring voice agents; results feed dashboards and regression tests so a prompt or model change can be checked before it ships.
Why it matters
A voice agent that demos well can still fail on real calls. Systematic transcript evaluation catches hallucinations, missed intents, and dropped tasks before they cost you customers, and turns "it feels better" into measurable scores.
We wire evaluation into every voice-agent build so quality is tracked continuously, not guessed — part of shipping agents you can trust in production.
Where we use it
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How our team puts Call Transcript Evaluation to work in real projects.
FAQ
Call Transcript Evaluation — common questions
What is call transcript evaluation?
It is scoring an AI voice agent’s calls, from their transcripts, against criteria such as accuracy, task completion, policy adherence and tone. It measures whether the agent actually performs well in production rather than just in a demo.
How do you evaluate an AI voice agent at scale?
Transcribe the calls, then grade each transcript against a rubric either manually or with an LLM-as-a-judge that applies consistent criteria automatically. Specialized tools like Coval, Hamming AI and Langfuse run these evaluations and track results over time.
What should a voice-agent evaluation measure?
Common metrics include task success rate, answer accuracy, script and policy adherence, interruption and latency behavior, and caller sentiment. The exact rubric depends on the agent’s job — a booking agent and a support agent are judged differently.
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