VOICE MODELS
Voice AI
Local STT, TTS, VAD, and echo control that let devices hear and speak without the cloud.
EDGE SYSTEMS / §01 — VOICE
ON-DEVICE VOICE AI
Build
voice agents
for physical AI
An on-device AI platform for building voice agents that make devices speak, listen, and reason locally.
LOCAL RESPONSE700ms
CLOUD CALLS0
DATA EGRESS0
§02 — Runs on the hardware you ship
NVIDIAAMDIntelQualcommRaspberry PiWebAssembly
§03 / Fig. 03 — Signal path
Speech, reasoning, and response run on-device.
SPEECH → REASONING → RESPONSE
700 ms round trip · 0 packets egress
700 ms round trip · 0 packets egress
FIG. 03 — Full-duplex voice loop. Mic → STT → SLM (+ memory, guardrails, tools) → TTS → speaker, entirely local. No request ever leaves the device.
§04 — WHY ON-DEVICE
Physical AI cannot wait on a cloud round trip.
THE CLOUD WAY
Mic → network → datacenter → back.
LATENCY1–2s
DATALEAVES THE DEVICE
OFFLINENEEDS CONNECTIVITY
Every utterance leaves the building. One dropped packet, one silent failure.
THE EDGEAI WAY
Mic → speaker, all on the same chip.
LATENCY700ms
DATA0 PACKETS EGRESS
OFFLINEWORKS OFFLINE
Nothing leaves the chip. Speech, reasoning, memory, tools, and response stay on-device.
FIG. 04 — Cloud vs. on-device. Measured on reference NVIDIA Jetson Orin hardware, idle system, 8B int4 model.
§05 — PRODUCTS
Everything an on-device voice agent needs.
AGENT ORCHESTRATION
Agent Harness
C++ runtime to create autonomous agents that reason, plan, and execute multi-step workflows.
EDGE MODELS
Model Catalog
Edge-ready SLMs selected, quantized, and compiled for local reasoning on embedded hardware.
1B – 35BOPTIMIZED PARAMETERS
FIG. 05 — Product overview. Voice AI, agent runtime, and edge models: one stack for devices that need to speak, listen, and reason locally.
§06 — Meet Friday
An AI that's yours alone.
Friday is a personal assistant that lives entirely on your device — brilliant, always on, and answerable to no one but you.
“Friday, what did I miss today?”
Friday
Two emails worth reading — and a 4 p.m. you almost forgot.
Always on-device — nothing you say ever leaves this machine
Early access
§07 — BY THE NUMBERS
The constraints physical AI buyers care about.
700ms
LOCAL RESPONSE
0
DATA EGRESS
1B–35B
MODEL RANGE
Runs on
FIG. 06 — Telemetry baseline. Measured on reference NVIDIA Jetson Orin hardware, idle system, 8B int4 model. Latency is the full mic-to-speaker response loop.
§08 — IN THE FIELD
One platform. Every physical frontier.
FIG. 01
Robotics
Talk to your robots. Spoken commands, understood on-device.
FIG. 02
Automotive
An in-cabin co-pilot that works with no signal.
FIG. 03
Aerospace
A mission voice agent where the radio can't reach.
FIG. 04
Defense
Private voice AI in comms-denied environments.
§09 — From the lab
Notes from the team.
Jun 22, 2026 · jetson
How to build an offline voice assistant on NVIDIA Jetson Orin
A builder's guide to running a fully offline voice agent on Jetson Orin — local STT, an SLM, and TTS, with real latency numbers and where the DIY Whisper + llama.cpp + Piper stack breaks down.
Read→Jun 20, 2026 · on-device-ai
Why we ripped cloud voice out of our robots: from Deepgram + OpenAI + ElevenLabs to on-device
We started building robot voice the easy way — Deepgram, OpenAI, and ElevenLabs glued together in Python. It demoed great and fell apart in the real world. Here's everything that went wrong, and why we're rebuilding the whole stack on-device.
Read→Jun 10, 2026 · open-source
turboquant.cpp: near-optimal vector quantization in 400 lines of C++, no training required
We open-sourced turboquant.cpp, a C++23 implementation of TurboQuant: compress embeddings to 1-4 bits per coordinate with provable distortion bounds — no training, no codebooks.
Read→FIG. 08 — Engineering notes, benchmarks, and field reports.
LIVE · ON-DEVICE
Put a voice on your machine.
Book a 30-minute technical demo. See it run on your hardware.