Voice notes, call recordings, and in-app dictation are only useful once they're accurate text. We integrate Whisper into existing products — transcription pipelines, call analytics, voice-driven features — scoped to your accuracy, latency, and privacy requirements instead of a generic speech-to-text checkbox.
Whisper is easy to call once. Turning it into a reliable feature — with real audio, real accents, and real latency budgets — is where most DIY integrations stall.
Chunking, format conversion, and streaming or batch processing built around your actual audio sources — not a demo file.
Prompt hints, vocabulary handling, and post-processing to reduce errors on names, jargon, and accents specific to your users.
Model size, chunking strategy, and caching decided against your volume and response-time needs before you commit to an approach.
Options ranging from hosted API calls to on-device or self-hosted transcription for audio that can't leave your infrastructure.
Clean integration code and documentation so your in-house team can extend the pipeline without reverse-engineering it.
Scoped to the feature you're shipping — not a fixed package you'll pay for and never use.
In-app recording to accurate text for notes, messages, or content creation features.
Recorded or live call audio turned into searchable transcripts with speaker segmentation and summary layers.
Language detection and transcription across the languages your users actually speak, not just English.
Dictation, voice search, or voice commands wired into your existing product flows.
Queueing, retries, and cost controls so transcription holds up under real production volume.
Very good on clear audio and common languages, and it degrades gracefully on noisy audio or heavy accents compared to older speech-to-text tools. For domain-specific accuracy — names, jargon, product terms — we tune prompts and add post-processing rather than relying on the raw model output.
Yes. That's one of the most common requests — transcription plus speaker separation, summaries, and searchable call history layered on top, integrated with whatever call or CRM system you're already using.
Yes. Whisper handles a wide range of languages and can auto-detect the spoken language, which we account for in the pipeline design if your users aren't all speaking one language.
Yes, smaller Whisper models can run on-device or self-hosted when audio can't be sent to a third-party API. We scope this against your accuracy and latency requirements early, since on-device tradeoffs are real.
Tell us what audio you're working with and what you need transcribed or analyzed — we'll scope an approach on a free call.