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Text-to-Speech API
Our first text-to-speech model, AssemblyAI-TTS-1, is built for voice agents, on live calls. It sounds natural, responds fast, and keeps callers on the line.
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Turn every call into structured insight—accurate Speech-to-Text, speaker labels, sentiment, entity detection, and summaries from a single API.
Thanks for hopping on—I know things are busy over at Northwind right now.
No problem. We're mainly trying to cut onboarding time below 30 days.
Got it. Most teams your size get that down to about two weeks with us.
Speaker diarization separates each voice so talk ratios, sentiment, and coaching map to the right person—even through interruptions.
Get sentiment, entity detection, topic detection, and summaries from the same transcript request—no separate NLP pipeline to stitch together.
Use LLM Gateway to generate coaching scorecards, call summaries, and custom insights directly from a transcript, with no model providers to manage.
36%
improvement in close rate
“The new Universal-3.5 Pro speech model from AssemblyAI is best so far in terms of accuracy, latency, and language switching.”
80%
increase in customer satisfaction
“Assembly has saved us countless hours managing models, and provided exceptional accuracy.”
36%
improvement in close rate
“The new Universal-3.5 Pro speech model from AssemblyAI is best so far in terms of accuracy, latency, and language switching.”
80%
increase in customer satisfaction
“Assembly has saved us countless hours managing models, and provided exceptional accuracy.”
One request returns a transcript with speaker labels, sentiment, and entities already attached. Add LLM Gateway to turn that transcript into summaries or scorecards—no model hosting to manage.
Start building freeNo credit card required
Entity error rate is the share of names, companies, numbers, and key terms the model drops or garbles—exactly what conversation intelligence is built to surface.
Entity error rate (NEER) on the voice-agent caller corpus: how often the names, addresses and numbers an agent acts on come out wrong.
*Lower is better*Source: AssemblyAI realtime benchmarks technical report v2026.09 (assemblyai.com/benchmarks), data 18 Aug–24 Sep 2026.
