Building Voice AI Agents for India with Sarvam and Vision Agents

AI is getting deployed everywhere, but most of the models powering these systems were built in the US, trained on English-heavy data, and run on infrastructure you don't control. That works fine until it doesn't, and for a lot of teams building in regions like India, it already doesn't.

What sovereign AI actually means

Sovereign AI isn't a buzzword. It's a real constraint that matters for governments, enterprises, and any team where data residency, language fidelity, and infrastructure control aren't optional.

It means your AI runs on compute you control, in a region that matters to your users, with models that actually understand how those users speak.

Sarvam AI

Sarvam AI is built for India. It's a full-stack AI platform — LLM, speech-to-text (STT), and text-to-speech (TTS) — running on sovereign compute, built to handle Indian languages, accents, and the nuance that comes with them.

That last part matters more than it sounds. Indian English is different. Hindi, Tamil, Telugu, Bengali — these aren't afterthoughts in Sarvam's models, they're the point.

The performance is also there. Sarvam's models sit at frontier performance levels, so you're not trading capability for compliance.

Integrating Sarvam with Vision Agents

Vision Agents is an open-source framework for building and deploying AI agents. With the Sarvam integration, you can now plug Sarvam's entire stack directly into your agent — STT, TTS, and LLM — in a few lines of code.

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async def create_agent(**kwargs) -> Agent:
    """Create the agent with Sarvam STT, TTS, and LLM."""
    agent = Agent(
        edge=getstream.Edge(),
        agent_user=User(name="Sarvam Agent", id="agent"),
        instructions=(
            "You are a helpful multilingual voice assistant. "
            "Reply in the same language the user speaks. "
            "Keep replies short and conversational."
        ),
        stt=sarvam.STT(language="hi-IN"),
        tts=sarvam.TTS(language="hi-IN", speaker="shubh"),
        llm=sarvam.LLM(model="sarvam-m"),
    )
    return agent

That's a fully multilingual voice agent that listens, thinks, and responds in Hindi — or whatever language you configure.

It's not a locked-in setup either. You can use as much or as little of the Sarvam stack as you need. Want to use Sarvam for STT but a different LLM? That works. Vision Agents is designed to be composable.

Everything else you'd expect from a production agent setup is also there — function calling, MCP tools, and full infrastructure control. Deploy on Docker, Kubernetes, or your existing stack. Your data doesn't leave your infrastructure.

Getting started

• Docs and quickstart: visionagents.ai/introduction/quickstart
• Learn more: visionagents.ai

If you're building voice agents for Indian users, or any market where language and data sovereignty actually matter, this is worth a look.