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ai agents

AI Voice Agent Development

Voice agents, chat assistants and LLM automation that feel like teammates — latency-engineered by the team behind our calling and live streaming products.

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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 that joins your calls and rooms like any other participant, in your own app or alongside the AI agents in our platform, Gravix Cloud. That means your agent works in any app that already has audio, with the latency budget (700 ms to first audio) designed in from the start, not bolted on.

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.

  • Works in the app you have

    Flutter, iOS, Android and web, with your current calling provider or with Gravix Cloud.

The stack

Full-stack, one team — designed in Figma, built in Flutter & Go, run in production. No hand-offs to strangers.

  • Flutter
  • Go
  • Python
  • OpenAI
  • Deepgram
  • ElevenLabs
  • Gravix Cloud
  • Redis

Fixed timelines, visible progress

  1. Discover

    We define the agent's job, the languages, and the latency budget that makes it feel human.

  2. Prototype

    A working voice loop in weeks — real models, real audio, real interruptions.

  3. Build

    Hardened pipeline: retries, monitoring, fallback models and cost controls.

  4. Launch & tune

    Recorded-session review, prompt iterations and the metrics dashboard all set up.

Questions, answered

Do you build the models?
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.
How fast does the agent respond?
Our budget is about 700 ms from the end of the user's turn to first audio — fast enough to feel like a conversation.
Can the user talk over the agent?
Yes. Interruption handling is a first-class feature: audio, transcript and semantic barge-in.
What platforms does it run on?
Any app with audio: Flutter, iOS, Android, web, and telephony gateways. The agent joins a call like any other participant, so integration is minimal.
Can you also build a plain chat assistant?
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.

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