// AI agents
AI Voice Agent Development
Voice agents, chat assistants and LLM automation that feel like teammates — latency-engineered on the same real-time stack as our streaming products.
// What you get
Why teams choose us
AI voice agent development sounds like a model problem; it is really an engineering problem. The models are excellent and improving weekly. What separates agents that feel natural from agents that feel like automated call centers is the pipeline around them: streaming speech-to-text, an LLM that knows when to stop talking, text-to-speech that starts in milliseconds, and interruption handling that treats the user's voice as the priority.
We build real-time voice AI on WebRTC, the same media layer our streaming products use, and the same real-time stack as our own platform, Gravix Cloud. That means your agent works in any app that already has audio, with the latency budgets (700 ms to first audio) baked into the architecture, not bolted on.
// Included
What's included
Streaming STT → LLM → TTS
A pipeline where every stage streams — nothing waits for a full utterance.
Interruption handling
Barge-in at audio, transcript and semantic levels, so users can speak over the agent.
LLM chatbot development
Chat assistants with your docs, your product data and your guardrails — with or without voice.
Latency budgets as a spec
A written budget for every millisecond — from mic to model to speaker.
Agent observability
Barge-in rate, dead-air ratio and completion metrics you can act on.
WebRTC transport, ready-made
Works in Flutter, iOS, Android and web — on self-hosted or Gravix Cloud infrastructure.
// Our stack
The stack
Full-stack, one team — designed in Figma, built in Flutter & Go, run in production. No hand-offs to strangers.
// How we work
Fixed timelines, visible progress
Discover
We define the agent's job, the languages, and the latency budget that makes it feel human.
Prototype
A working voice loop in weeks — real models, real audio, real interruptions.
Build
Hardened pipeline: retries, monitoring, fallback models and cost controls.
Launch & tune
Recorded-session review, prompt iterations and the metrics dashboard all set up.
// FAQ
Questions, answered
No — and you would not want us to. We integrate best-in-class speech and LLM models and engineer the real-time system around them.
Our budget is about 700 ms from the end of the user's turn to first audio — fast enough to feel like a conversation.
Yes. Interruption handling is a first-class feature: audio, transcript and semantic barge-in.
Any app with audio: Flutter, iOS, Android, web, and telephony gateways. Transport is WebRTC, so integration is minimal.
Yes — LLM chatbot development with RAG over your knowledge base is the non-voice half of our AI practice.
Your customers deserve an agent, not a phone tree.
Tell us about your project — we'll reply with a plan, a timeline and a straight answer.
Voice agents run on WebRTC infrastructure and Go backends. Our article on building real-time voice agents walks through the architecture in depth.