Technology
This platform is not a wrapper around a third-party voice API. We run a proprietary speech-to-text to LLM to text-to-speech pipeline, tuned for Indian languages and code-mixed speech, with an agentic layer that decides what a conversation should do next.
How a call moves through the system
Audio is processed as a live stream rather than a sequence of request and response calls, which is what lets an agent listen while it is still speaking and react to an interruption mid-sentence.
Streaming audio ingestion over SIP or WebRTC, with echo cancellation and barge-in detection so a caller can interrupt at any point.
A proprietary speech-to-text model tuned on Indian languages and code-mixed speech, so a sentence that moves between Hindi and English mid-thought is transcribed correctly.
An agentic orchestration layer holds the conversation state, calls business tools such as a CRM or scheduling system, and decides the next thing the agent should say or do.
Low-latency text-to-speech generates a full-duplex response, meaning the system can keep listening for an interruption while it is still talking.
Every call is passed to Vantage, which scores, tags, and reports on it, so the next conversation starts from a better script.
Analytics
Vantage is patent-pending and runs as a six-stage agent pipeline over every recorded conversation, independent of which product in the suite generated the call.
Language
The speech and language stack is trained to handle Indian languages and code-mixed speech directly, rather than translating to English and back, which is where latency and meaning are usually lost.
Research
Alongside our production pipeline, a dedicated research track is building a speech-to-speech foundation model: an architecture derived from open full-duplex research, adapted rather than trained from scratch, with a proprietary audio encoder layered on top.
| Roadmap item | Detail |
|---|---|
| Target scale | Approximately 800 million parameters |
| Compute | IndiaAI H200 SXM GPU allocation |
| Team | Six-person research and engineering team |
| Timeline | 7 to 9 months, from architecture adaptation to evaluation |
| Approach | Adapting an open full-duplex architecture with a proprietary audio encoder, rather than training a model from scratch |
This is the primary driver behind our compute needs and the basis of our NVIDIA Inception, Google Cloud, and AWS credit applications.
Call audio and transcripts are encrypted in transit and at rest.
Deployments can be scoped to keep customer voice data within India, where that is a regulatory requirement.
Role-based access to recordings and transcripts, with a full audit trail for regulated deployments.
