Every model - turn-taking, STT, LLM, and TTS - trained from scratch and optimized for commodity CPUs. No GPU dependency. Sub-450ms end-to-end.

The Only CPU-First Voice AI

Most voice AI platforms follow the same recipe: stitch together a STT API from one provider, an LLM from another, a TTS from a third - each running on expensive NVIDIA GPUs, each adding 200–300ms of network latency. The result is a fragile cascade that costs too much and feels unnatural.

We took a different approach. Every model - Vela (turn-taking), Psst (noise suppression), our fine-tuned LLM, STT, and Versa (TTS) - is trained from scratch and hand-optimized for ARM and x86 CPUs. No GPU overhead. No third-party dependencies. Full control over quality and latency.

The same cascade architecture, rebuilt model by model for maximum efficiency. Sub-450ms on a standard server.

Our models are trained on MareNostrum 5, one of Europe's most powerful supercomputers, through the Barcelona Supercomputing Center AI Factory program. We work closely with NVIDIA (Inception partner) and AWS to push CPU-based inference to its limit. This is European deep tech - building voice AI infrastructure from the ground up.

Real-World Applications

Developer First

Integrate Lokutor in minutes with our SDKs and comprehensive documentation.

import { VoiceAgentClient, VoiceStyle, Language } from '@lokutor/sdk';

const client = new VoiceAgentClient({
  apiKey: 'your-key',
  prompt: 'You are a helpful and friendly AI assistant.',
  voice: VoiceStyle.F1,
  language: Language.ENGLISH,
  visemes: true,
});

client.on('transcription', (text) => console.log('You:', text));
client.on('response', (text) => console.log('Agent:', text));
client.on('visemes', (visemes) => animateMouth(visemes));
client.on('status', (status) => console.log('Status:', status));

await client.startManaged();

from lokutor import VoiceAgentClient, VoiceStyle, Language
import os

client = VoiceAgentClient(
    api_key=os.environ['LOKUTOR_API_KEY'],
    prompt='You are a helpful and friendly AI assistant.',
    voice=VoiceStyle.F1,
    language=Language.ENGLISH,
)

@client.on('transcription')
def on_transcription(text):
    print(f"You: {text}")

@client.on('response')
def on_response(text):
    print(f"Agent: {text}")

@client.on('status')
def on_status(status):
    print(f"Status: {status}")

client.start_conversation()

Get Started

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Read Documentation →