Inference your stack was never built to run.
You already capture the audio. Your pipeline transcribes it, routes it, and discards everything about the speaker that wasn't a word.
Your customers keep asking what your platform can tell them about the person on the call, and the honest answer is nothing beyond what they said. Transcription is a lossy transform. The acoustic layer carries respiratory support, phonation stability, prosody and timing, and none of it survives the conversion to text. Building the capability yourself means population-scale audio paired to medical records under an IRB framework, which is a five-year problem, not a sprint.
One endpoint, one call, structured JSON back. Ask for a single sign or a full bundle. Every response returns a score, a level, and an audio quality read that tells you when the sample wasn't good enough to trust.
Runs in parallel to your transcription path on the same audio. Nothing about your core system changes.
Any human voice audio, ten to 120 seconds. You send the same buffer you're already sending to ASR. We return signal alongside your transcript rather than in place of it.
Available as a REST endpoint or through MCP for agent-based products.
No diagnosis. No automated action on anyone's behalf. We return signal to your system and what you do with it is your product decision, not ours.
We don't compete with your speech models and we don't want your transcript. This is a layer underneath what you already do well.
See it against your own workflow.
We'll walk through what applies in voice ai platforms, what doesn't, and where the limits sit, before any commitment.
request a walkthroughAmplifier's voice analysis is not FDA approved and is not a diagnostic device. Narrative interpretations are provided to qualified care or research staff only, never as automated alerts and never directly to the person analyzed.
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