Fast, accurate speech APIs at half the usual price.
Fast, accurate, cost-effective speech APIs: proprietary and open-weight models behind one gateway, with speaker intelligence and LLM processing built in.
Everything after the microphone.
Batch and streaming transcription with word-level timestamps.
Who spoke, when, separated into speaker turns.
Attach turns to known, enrolled voices.
Formatting, entities, sentiment, topics on the same job.
Summaries, extraction, classification on transcripts.
Conversational sessions with interruption handling.
Feels familiar on purpose.
Ultrafield speaks the API you already use: OpenAI-compatible audio endpoints, Bearer auth, idempotency, resumable SSE, signed webhooks. Point your client at a new base URL and send audio.
$ curl https://api.ultrafield.ai/v1/audio/transcriptions \ -H "Authorization: Bearer $ULTRAFIELD_API_KEY" \ -F file=@support-call.wav \ -F model=ultrafield-asr-1 \ -F processing=["diarization","entities","summary"] HTTP/1.1 200 OK { "text": "Thanks for calling. Could I get the order number?…", "words": [ { "w": "Thanks", "start": 0.12, "end": 0.34 }, … ], "speakers": [ { "id": "S1", "turns": 11 }, { "id": "S2", "turns": 9 } ], "entities": [ { "type": "ORDER_ID", "span": "A-4417" } ], "summary": "Support call about a delayed order; replacement shipped.", "usage": { "audio_seconds": 512, "llm_tokens": 486 } }▌
Choose the engine. Keep the API.
One request. The whole pipeline.
Chain formatting, speakers, entities, sentiment, and LLM steps in a single request. Independent steps run in parallel; you get one job, one webhook, one structured result.
Predictable at volume.
Prepaid credits, drawn down per processed second. Pay for the stages a request actually runs, not seats, not bundles.
Why Ultrafield.
Transcribe across 100+ languages with automatic language detection.
Competitive with the best ASR available. Evaluate on your own audio and pin model versions for reproducibility.
Fast response times on optimized, chained infrastructure. Transcribe an hour of audio in as little as 10 seconds.
Your data is never stored beyond ephemeral processing. We keep only aggregated usage stats for account management.
We never train models on customer audio, transcripts, or outputs. Zero-data-retention options available.
Choose US or EU data centers for Enterprise workloads.
Next: conversation.
Realtime voice-agent APIs are in development: natural turn-taking, interruption handling, tool execution, on the same platform and the same event conventions.
Questions, answered.
Ultrafield's own models alongside open-weight options including Whisper large-v3 and large-v3-turbo. Choose directly, or let the pipeline match a model to the workload.
Yes. Uploaded files, audio URLs, and low-latency streams share one API surface: archives, live captions, analytics, conversational products.
Diarization, speaker identification, formatting, entity detection, sentiment, and LLM processing, composed in one request.
Diarization separates a recording into speaker turns; identification attaches turns to enrolled voices when your workflow needs it.
Chain a transcript into hosted models such as GPT-OSS-120B for extraction, classification, or summaries. A gateway to OpenAI and Anthropic models is on the roadmap.
Per-minute rates published on the pricing page, designed to stay low as volume grows. Volume rates for sustained workloads.
Give your product better ears.
Move from raw audio to accurate transcripts, speakers, entities, sentiment, and application-ready output through one pipeline.
Batch and streaming · Proprietary and open models