LiveKit, a platform for building voice, video and physical AI agents, has appointed Tom Davies as Chief Revenue Officer to lead its global revenue organisation. Davies joins from Grafana Labs, where he served as VP of Sales for the West, and previously spent six years at Snowflake leading vertical sales organisations. The appointment comes as LiveKit scales to meet growing enterprise demand, with more than 10% of Fortune 500 companies currently using its platform. The company also announced new leadership hires including Megan Barros as Regional VP of Sales, Cameron Huang as VP of Finance and Michelle Schroeder as VP of Marketing. LiveKit raised $100 million in a Series C round at a $1 billion valuation in January 2025, powering AI applications for companies including SAP, Tesla, OpenAI and Spotify.

Telnyx has launched LiveKit on Telnyx, a fully hosted platform for deploying voice AI agents with reduced costs and ultra-low latency. The platform allows developers to run existing LiveKit agents on Telnyx-owned infrastructure without code changes. By owning the entire infrastructure stack—carrier network, GPU clusters and telephony—Telnyx offers 50% lower speech-to-text and text-to-speech costs compared to LiveKit Cloud. The company is waiving session fees during the beta period, eliminating the current $0.01 per minute charge LiveKit customers typically pay. The platform achieves sub-200ms round-trip time by hosting speech models on colocated GPU infrastructure across 18 global points of presence. It includes enterprise telephony features and compliance standards including HIPAA, PCI and SOC 2. LiveKit on Telnyx is now available in beta.

Open-source wake word training in a single command. Wake words are the short spoken phrases, like "Hey Siri" or "Alexa", that activate a voice-enabled device or agent. They're the first step in any hands-free voice interaction, and getting them right matters: too sensitive and they fire constantly, too strict and users have to repeat themselves. Today LiveKit Incorporated is launching livekit-wakeword, an open-source wake word library built for simplicity and speed. Why LiveKit Incorporated built this. If you've tried training wake word models before, you know the pain: * Existing codebases are outdated, with broken dependencies everywhere. * Documentation is sparse or nonexistent, so training new models requires hours or even days of reverse-engineering. And even if you manage to train a model, you still end up with one that false-triggers constantly because you used the vanilla settings the authors provided. LiveKit Incorporated built livekit-wakeword to fix all of this. Now you can train your own wake word model from scratch, locally, with a single command. Use cases. Custom wake words unlock hands-free voice activation across a wide range of applications: * Voice agents: Give your AI agent a branded activation phrase ("Hey Jarvis," "OK Chef") instead of relying on a generic keyword. * Smart home assistant: Train a custom phrase for your home setup without depending on cloud services. * Robotics: Activate a robot with a spoken command in noisy warehouse or factory environments. * Kiosks & accessibility devices: Enable hands-free activation for retail, healthcare, or public-facing hardware. * In-car & embedded systems: Trigger voice control in vehicles or IoT devices running on constrained hardware. Performance. Even though its library is simple and fast, LiveKit Incorporated didn't sacrifice accuracy. Compared to openWakeWord, livekit-wakeword achieved dramatically better results across every metric: * 100x fewer false positives per hour * 60x lower detection error * 86% vs 69% recall | Metric | livekit-wakeword | openWakeWord | | False positives per hour (FPPH) | 0.08 | 8.50 | | Detection error tradeoff (AUT) | 0.0012 | 0.0720 | | Recall | 86% | 69% | FPPH measures how often the model incorrectly fires when no wake word was spoken - lower is better. AUT (area under the DET curve) captures the overall tradeoff between false positives and missed detections. See the full comparison for DET curves, test conditions, and detailed methodology. How it works. Under the hood, livekit-wakeword generates thousands of synthetic training samples using text-to-speech, then applies realistic audio augmentations (background noise, reverb, gain variation) to simulate real-world conditions. A lightweight convolutional-attention classifier trains on top of pre-computed audio embeddings, producing a small, fast model that generalizes well beyond its training data. Since its exported models use the same ONNX format and inference pipeline as openWakeWord, they're fully compatible. Your Home Assistant or legacy projects still work with zero changes. Part of the LiveKit ecosystem. livekit-wakeword is designed to work seamlessly with the LiveKit platform. Use a wake word to trigger a LiveKit Agent session. The wake word model runs locally on-device with minimal latency, and once activated, LiveKit handles the realtime audio streaming to your agent. Start building with livekit-wakeword. To train a new wake word model, install the library and run setup: 1 # install livekit-wakeword with training, evaluation, and export extras 2 pip install livekit-wakeword[train,eval,export] 3 4 # download required embedding models and datasets 5 livekit-wakeword setup Then create a config file for your wake word: 1 model_name: hey_robot 2 target_phrases: 3 - "hey robot" 4 5 n_samples: 10000 # synthetic training samples per class 6 model: 7 model_type: conv_attention # its new conv-attention classifier 8 model_size: small 9 steps: 50000 # training steps Check out the README for the full list of config options. Once your config is ready, you can train your model with a single command: 1 # generates synthetic data, augments, trains, and exports to ONNX 2 # your model will be saved to ./output/hey_robot/hey_robot.onnx 3 livekit-wakeword run configs/hey_robot.yaml That single command handles everything: synthetic data generation, augmentation, training, and ONNX export. You'll get a production-ready model file you can use right away. The exported model is a standard ONNX file, fully backward compatible with openWakeWord, so it drops into Home Assistant or any existing openWakeWord integration with zero changes. To run detection, just load the model and feed it audio: 1 from livekit.wakeword import WakeWordModel 2 3 # load your exported ONNX model 4 model = WakeWordModel(models=["hey_robot.onnx"]) 5 6 # feed 16kHz audio frames (int16 or float32) 7 scores = model.predict(audio_frame) 8 if scores["hey_robot"] > 0.5: 9 print("Wake word detected!") LiveKit Incorporated also provide a WakeWordListener that handles all the audio capture for you, so you can listen from the microphone without writing any audio code yourself: 1 from livekit.wakeword import WakeWordModel, WakeWordListener 2 3 model = WakeWordModel(models=["hey_robot.onnx"]) 4 5 # captures audio from the microphone and runs detection automatically 6 async with WakeWordListener(model, threshold=0.5) as listener: 7 while True: 8 detection = await listener.wait_for_detection 9 print(f"Detected {detection.name}!") For a complete example that uses wake word detection to spawn a LiveKit agent, check out hello-wakeword. Other runtimes. For production deployments, LiveKit Incorporated currently support Rust. More runtimes are on the roadmap. Future directions. On the hardware side, the current architecture already runs comfortably on single-board computers, but LiveKit Incorporated is taking it further. LiveKit Incorporated is building an end-to-end model that removes the need for a separate embedding model, making it small enough to run directly on ESP32 and other embedded microcontrollers. Want to get involved? Check out the repo and join its developer community to share what you're building.