Wake words, keyword spotting, speech-to-intent and more. Set up in hours.
Inference runtime written from scratch in Rust — hand-tuned for ARM NEON and x86 SIMD. Production binaries in the hundreds of kilobytes, not megabytes. INT8 quantization by default. Predictable latency. Bounded battery cost.
Audio never leaves the user's phone. Models are encrypted at rest and bound to your license through per-customer key derivation. No cloud round-trip. No per-request fees. Works offline by default.
Wake words, keyword vocabularies, and intent contexts are trained per customer from your spec. No generic assistant retrofitted to your product — a model that understands what your users actually say.
Compose them, ship them, and get a privacy story enterprise customers expect — without giving up latency or battery.
Write a context spec — intents and slots in YAML — and we train a model that maps speech directly to structured intents. Audio to intent in one inference, no transcript stage. Lower latency, lower memory, better accuracy on your domain.
Always-listening, sub-millisecond per frame on modern phones. The foundational primitive — gates wake-word and KWS for power efficiency, drives interruption logic, supports VAD-only use cases.
Train any wake phrase — "Hey YourBrand", "OK Product", multi-word activations. Robust to noise, distance, and accents through synthetic data and augmentation. Tunable confidence scoring.
Detect a fixed vocabulary of voice commands — play, pause, next, louder, stop. Multi-class classifier with per-keyword confidence. Lower latency and compute than full ASR for closed-vocabulary control.
Real-time English transcription with low-latency partial results, plus a higher-accuracy batch mode with punctuation and capitalization. Same on-device runtime — no cloud.
Pipeline supports English, French, German, Portuguese, Italian, and Spanish out of the box. Additional languages roll out as voice corpora and customer demand align.
On-device speech synthesis with multiple voices. For in-app responses, accessibility, and offline assistant experiences.
Recognize enrolled speakers for personalization and access control. "Who spoke when" segmentation pursued where a quality on-device model is identified.
Same model artifacts, same Rust runtime — new platform adapters. Tell us what you need to ship on and we'll prioritize.
no_std-compatibleEither ship a large generic engine that bloats your app and drains battery, or send audio to the cloud and pay per request. Our runtime is purpose-built for tiny binary footprint and predictable latency on real ARM CPUs — every model shipped on-device, trained to your domain, licensed per app.
Tell us what you want to build and which devices it has to run on. We'll get back within a business day.
Tell us a bit about what you're building. We'll be in touch within a business day.
We'll reach out within a business day to schedule a quick call and get you set up.