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AI service — AI Medical

Medical speech to text

Transcribe consultations, dictation and case conferences in our GPU clusters, with speaker labels and the clinical vocabulary your specialty actually uses — and a dedicated private cluster or on-premise option where recordings may not leave the building.

Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.

Speech comes back as timed, speaker-labelled text. Clinical vocabulary is where general transcription fails — drug names, dosages, anatomy, local abbreviations — so the vocabulary is fitted to your specialty and to the terms your own recordings actually contain.

Audio is among the most sensitive material a hospital holds, so deployment follows your governance: the shared pipeline in our GPU clusters, a dedicated private cluster, or an on-premise installation. Transcripts return with timestamps and speaker turns, ready for a summarization step or for the record, with a per-job audit trail.

Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.

What we size for

We build the medical speech to text pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.

AI models for Medical speech to text →

Common use cases

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