In Hindi-English calls, latency is the difference between "this feels like a person" and "this is clearly a bot." If the agent pauses a beat too long after the caller speaks — or talks over them — the conversation breaks. For Hinglish specifically, where speakers switch languages mid-sentence, the stack you choose matters even more.
This is the low-latency stack our fast Hinglish templates run on, and why each piece is there.
The stack
Our low-latency Hinglish templates use a deliberately lean pipeline tuned for speed:
| Stage | Provider | Why |
|---|---|---|
| STT (speech-to-text) | Deepgram | Fast, accurate streaming transcription that holds up on Indian-accented English and Hindi |
| LLM (reasoning) | Gemini 2.5 Flash-Lite | The lowest-latency tier that's still good enough for goal-driven calls |
| TTS (text-to-speech) | Cartesia (Hindi voice) | Natural Hindi prosody with low time-to-first-audio |
The language mode is Hindi + English (hindi_english), so the agent mirrors whatever the caller uses and switches naturally between them.
Why this combination (and not a heavier one)
For a richer conceptual treatment of mixing languages, see code-switching in Hindi-English voice AI. The practical point for speed: every component here is chosen for time-to-first-token / time-to-first-audio, not maximum capability.
- A streaming STT (Deepgram) emits partial transcripts early, so the LLM can start thinking sooner.
- Flash-Lite is intentionally a smaller, faster model — for a focused task like lead qualification or an OPD enquiry, you don't need a frontier model, and the latency win is large.
- A low-latency TTS (Cartesia) starts speaking sooner, which is what callers actually perceive as "responsiveness."
If you instead want the most natural speech-to-speech feel and can trade a little control, a native-audio model is the other route — we compare that in Gemini Live 2.5 HD voice AI explained. The low-latency pipeline above is the pick when you want speed plus explicit control over the conversation.
Templates built on this stack
Two ready templates ship on the fast Hinglish stack:
- Sales Outbound Agent (Fast) — lead qualification and follow-up. You set your company name, the product/service, and the lead source; it dials and qualifies.
- OPD / Information Assistant (Fast) — answers availability and timing-style enquiries, with a clean transfer-to-human path.
Each takes a couple of variables and is ready to run — no prompt engineering required to get started.
Who this is for
- Outbound sales / lead-qualification at volume, where every extra 200 ms of lag costs engagement
- High-call-volume enquiry lines that need to feel instant
- Any India-facing use case where callers speak Hinglish, not pure Hindi or pure English
For the bigger picture on building Indian-language agents, see our Indian-language voice AI overview.
Try it
Spin up a fast Hinglish agent on the Edesy voice AI platform — pick a low-latency template, set your variables, and connect your telephony.