Same call. Three agents.
Transcript
- 0:01
One caller. One request. Three voice agents.
- 0:05
The pipeline agent relays her words through three vendors, and waits. The speech model is quick. VARTA answered from its cache before either one started.
- 0:15
A house number is where calls go wrong. One agent heard it wrong and asked again. One heard it wrong, and didn't ask. VARTA captured it, and confirmed it. By rule, not by luck.
- 0:29
An interruption. The pipeline talks over her. The speech model wanders off script. VARTA answers the question, and returns to the job.
- 0:40
Per call, that is forty-six percent cheaper. At a million calls a month, it is eighty-eight lakh rupees. Every month.
- 0:48
Seven layers answer first. The model is the exception. Every turn is traced, and the rules heal themselves from live calls.
- 0:57
Every voice agent waits for a model. VARTA mostly doesn't.
About this film
A bank customer updates her address. The cascaded pipeline relays her words through three vendors and leaves her waiting. The speech-to-speech model answers fast, mishears the house number and never checks. VARTA answers from cache, captures the address, confirms it by rule, handles her interruption and goes straight back to the job. Per call that is modelled at 46% cheaper with the vartavoice.ai cost estimator at a 60% cache-hit rate.
1:04 · · English captions
More films

Teaser · 56 s
The model can wait.
In a world where every voice agent waits for a model, one engine decided not to wait. Rules first, model last: seven layers, zero hesitation, the same voice at half the bill.

Explainer · 4 min
Train once. Run a million calls.
DAKSH uses a language model once per workflow, the way a programmer uses a compiler. VARTA runs every call on seven layers of code, asks the model last, and never lets it invent a route.

Walkthrough · 4 min
Configured once. Decided every turn.
Step by step through a bank KYC workflow: the configuration DAKSH writes at each step, and how it lets VARTA decide the call inside the engine instead of asking a language model.

Inside the engine · 47 s
One sentence through the VARTA engine
Follow one caller sentence through VARTA layer by layer: reflex, recognition and rules first, an LLM only when nothing else can answer, every step on one visible ledger.

For enterprise · 1 min
Why VARTA works for enterprise
Fluency no longer closes deals. Decisions by rule, writes to your ERP mid-call, live data volunteered only when a threshold is crossed, and a deterministic cost engine, in four markets.

Premium voices · 1 min
Keep your voices. Cut the bill.
Keep the premium voice you chose. VARTA replays the lines an agent has said before from cache, in the same voice, and sends only the new ones to your provider.

Layer 5 · 1 min
NITI: the rule engine inside VARTA
Every voice agent waits for a language model. VARTA mostly does not. NITI resolves a turn by rule in under two milliseconds, with the LLM as the last resort.

UPCHAAR and SHALYA · 2 min
How VARTA heals itself
Most voice agents stay as smart as the day they were written. VARTA’s two healing layers learn from every LLM rescue and propose structural fixes, checked before anything goes live.

Launch film · 24 s
Introducing VARTA
A voice-agent platform with a visible cost ledger: seven explicit layers, cached audio for repeated phrasing, and only the turns that need reasoning reach an LLM.