The architecture

Every other voice AI is three AIs in a trench coat.

Cascaded STT → LLM → TTS pipelines are what you get when you bolt open-source parts together. They can't hear prosody. They can't interrupt naturally. And they can't do Hinglish without hallucinating.

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Latency, measured

Where the 1.5 seconds go

Same task — customer says 'Haan ji?', agent replies. Cascaded stack vs. our native audio stack. Both measured from end-of-utterance to first audible word, on Mumbai-hosted workloads.

CASCADED PIPELINE · competitor avg2200ms
450ms
End-of-utterance detection
350ms
Speech-to-text (STT)
900ms
LLM reasoning
500ms
Text-to-speech (TTS)
End-of-utterance detection
Speech-to-text (STT)
LLM reasoning
Text-to-speech (TTS)
VOICEAI NATIVE · measured p95700ms
120ms
Interrupt detection
440ms
Audio→audio generation
140ms
First-byte render
Interrupt detection
Audio→audio generation
First-byte render
68%
lower first-word latency
vs. typical cascaded pipeline
+42%
conversation completion
customers stay on the line
0
dropped turns
native VAD; no end-of-utterance guessing
Barge-in done right

Human conversations interrupt. Your AI needs to, too.

Our native model listens while it talks. When the caller jumps in — "actually, make it tomorrow" — the agent stops mid-word and pivots. No second-long dead air. No awkward "sorry, could you repeat?"

120ms
barge-in detection
0%
cross-talk collisions
AI
00:12 · AGENT
Aap ko loan approve ho gaya hai sir, documents bhejne ke liye…
You
00:14 · CALLERBARGE-IN DETECTED
Ek second — what's the interest rate?
AI
00:14 · AGENTPIVOT · 180ms
Yes sir, interest rate aapka 11.5% per annum hai, reducing balance pe.
Cascaded stacks would show ~1.9s dead air at position 2.Ours: 0ms.
Honest comparison

Next to every other option.

We update this page when competitors ship. Last refreshed: April 2026.

CapabilityVoiceAICompetitor BCompetitor VCompetitor R
First-word latency (p95)700ms1.9s2.1s2.4s
Native speech-to-speechYesNoNoNo
Indian language accents22 + Hinglish switchingHindi + EngEn-IN onlyEn-IN only
Barge-in response time120ms600ms800ms1.1s
Hinglish code-switchMid-sentenceBrokenNoNo
Platform rate₹7.99/min₹8/min~₹19/min~₹28/min
Telco billingPass-through (₹0.85/min)OpaqueBundledBundled
Hosted in IndiaMumbai regionYesUS edgeUS edge
DPDP Act complianceFull (DPA, GO, DPO)PartialNoNo

Measured on identical Hindi-language outbound EMI-reminder workloads, 10,000 calls each.

The India-specific problem

Silicon Valley voice AIs were not built for this.

Generic voice agents trained on North American podcasts don't translate. The India-specific ones have a lot of work to do.

01
Hinglish code-switching
Callers switch languages mid-sentence. Most AIs hallucinate nonsense. We trained on authentic Indian conversational audio.
02
Regional accents
Punjabi-accented Hindi. Tamil-accented English. Not the same as textbook Hindi or American English.
03
Background noise
Calls happen at 8am in a Mumbai local. Our VAD is tuned for 8kHz phone audio, not studio conditions.
04
Names, places, numbers
Cascaded TTS butchers Indian names. Ours pronounces three-word Sanskrit names correctly.
05
Tier-2/3 contexts
The caller's idioms, metaphors, and referents aren't Stanford English. Our model was tuned for this.
06
Cultural formality
Knowing when to use "aap" vs "tum", or appending "ji" — this matters. We get it right.

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