Create, connect, and scale AI-powered voice experiences with seamless orchestration and real-time intelligence.
Take the speech models on their own, or let us run the whole voice agent. The same API either way, with open model weights on our GPUs or inside your own network.
Layer 01
Call an API, get speech models running on our GPUs. No model files, no rented cards, no inference engine to tune.
Buy this half on its own
Layer 02
Everything between the caller and the model. This is the hard part of voice AI, and almost none of it is the AI.
Runs entirely on layer one
Layer 03
A voice agent with no number is a demo. We provide the calling side, so your agent can be reached the way your customers already call you.
Connected before you launch
Our own orchestration product is our first customer, on the same endpoints you get. If it needs something, you get it too.
Owning both layers is how we can see which exact millisecond hurts and fix it in the model layer instead of working around it.
Change the model string and keep everything else. Every engine sits behind the same wire, so a new one is a registry entry.
Pick a business moment. See what the customer says, what the agent understands, and what your business gets back.
The customer chooses a time before the booking is changed.
Explore supported speech capabilities across Indian languages and build conversations that feel familiar from the first hello.
“A familiar language makes space for a better conversation.”
Selected language · speech coverage varies by capabilityNot a checkbox. No commercial inference provider hosts AI4Bharat, and the strongest Indic API keeps its speech models closed. If you need these languages inside your own network, this is a short list.
Marathi runs only on IndicConformer, the one permissively licensed self-hostable model that serves it, which is exactly why that engine is in the registry. English and the major European languages come through Whisper.
No open audio-to-audio model handles Marathi, or most of these languages, yet. Our two strongest positions do not overlap, and you would find that out on the first demo call anyway.
Four stages work together in real time. One clear experience for your customer. One observable system for your team.
Detect speech, silence, interruptions, and the language being spoken.
Use your instructions, knowledge, context, and approved tools.
Check status, book an appointment, route a call, or record an outcome.
Respond naturally, pause when a customer interrupts, and keep the conversation moving.
The model ecosystem
Every stage of a call runs on an open model with published weights and a permissive licence - in our cloud, or entirely inside your own network.
Talk to us
OpenAITranscription
Alibaba QwenLanguage models
AI4BharatIndic speech
Resemble AIVoice cloning
NVIDIAGPU inference
HexgradSpeech synthesis
SileroVoice activityFor developers
Anyone who has used the OpenAI or Together SDK can read this without opening the docs. Streaming is the normal case here, not a flag on the end of a call.
Streaming and batch, 22 Indian languages plus English and European.
Words land while the speaker is still talking: partial and final transcripts, per-word timings, and code-switching inside a single utterance. Four engines behind one wire, so you can pick by name or let the language pick for you.
26 voices, streamed as they are generated.
Raw PCM back over HTTP while the sentence is still being synthesised, so playback starts before it finishes. Real-time factor 0.102 on GPU and 0.345 on CPU, both comfortably ahead of playback.
One websocket, the whole conversation.
Open a session against a published agent and send microphone audio. Voice activity detection, endpointing, barge-in, tool calls and knowledge retrieval all run server-side; audio and events come back.
A tag on the page, and no audio code to write.
One custom element handles microphone permission, streaming, playback and interruption. Same runtime as the websocket API, with the audio plumbing already done.
These are the voice ids the registry ships today. Pass one straight through as the voice parameter.
“Your order shipped this morning and arrives on Thursday.”
Visual preview of the synthesis interface: pick a voice to see the id you would pass to the API. No audio is produced on this page. The playground makes the real call.
Hear it in the playgroundSee exactly how we bring your agent together, from the conversation brief to continuous improvement.
Together, we choose a clear use case: support, sales, reminders, appointments, or feedback. We define who the agent will speak to, what a successful call looks like, and when a person should take over.
You share your FAQs, policies, product information, and approved answers. We organise this knowledge and agree on how the agent should respond when an answer is missing.
We help you choose a supported language, voice, tone, and greeting. You review sample conversations and approve the agent’s boundaries and handoff rules.
We provide a physical SIM, eSIM, or activated business number for your calling setup. We connect the number to your agent and check the call routing before launch.
We connect the business systems your use case needs, such as a CRM, booking calendar, or order service. You approve what the agent can read and which actions it can perform.
We run sample calls with interruptions, accents, unclear requests, and missing information. Your team reviews the answers, system actions, and handoffs so issues can be corrected before launch.
You approve the tested agent and calling workflow. We publish that version, confirm the operating settings, and start with the scope your team is ready to manage.
We review transcripts, system activity, call outcomes, and usage with your team. Changes are tested and reviewed before a new version is published.
Production voice AI needs more than a convincing voice. It needs control, review, and answers when something changes.
A deployment names the version it is serving, so “what are callers hearing right now” is one line rather than two tabs. Roll back to any published version without a redeploy.
Transcripts are rebuilt from the same events the agent saw during the call, next to the tools it invoked and the documents it retrieved. A slow turn names the stage that was slow.
Compare a new version against the one it replaced, across the conversations each actually served. Where the sample is too small to support a conclusion, the panel says so rather than drawing an arrow.
Automated scoring and a human verdict sit in separate columns and never overwrite each other, so how often the two agree stays a number you can check before either reaches a customer.
Spend attributed to listening, understanding and speaking, per service and per meter, so a line on the bill can be traced back to the part of the pipeline that produced it.
Separate keys for the voice wire, for a public deployment endpoint, and for the assistant MCP surface. Each is revocable, inherits workspace membership at request time, and shows its plaintext exactly once.
Deployment
The fully self-hosted path is not a roadmap item. Transcription, the language model and the voice all run on hardware you own, and no audio leaves the network.
Managed, on our GPUs. One key and a model string, with nothing to deploy.
Fastest way to hear a model.Our containers inside your perimeter, in your region, still managed by us.
GPU-VM compose files ship in the repo.Every model self-hosted: faster-whisper, Kokoro and a quantised Qwen on one machine.
Working today, on hardware you own.Tell us the call your team handles every day. We’ll show you how Voice Orchestrator can listen, respond, and take the next step.
Talk to our teamCreate a key, copy an example, and make your first call. Your trace shows the time spent at each stage.
Sign in Read the docs