Train once. Run a million calls.

Transcript

1. 0:01

   Every time you talk to an AI voice agent, something is guessing. A model reads a long prompt, thinks for a few seconds, and makes up an answer. Most days, it's right. A business can't run on most days.

2. 0:17

   Almost every voice agent today works the same way. Everything the caller says goes to a large language model, with a long prompt. That's three to four seconds of waiting, a bill on every turn of every call, and an answer that can change from one call to the next, because the model works on probability, not on rules.

3. 0:39

   We looked at voice agents through a different lens. A good call centre doesn't let new agents improvise. It trains them first, then puts them on the floor, where they handle thousands of calls the same way, every time. So we split the voice agent in two. A training centre, and a floor. Design time, and run time.

4. 1:01

   The training centre is DAKSH. DAKSH uses a language model once, the way a programmer uses a compiler. You describe the workflow, say, a bank's KYC check. DAKSH turns it into steps, rules, the many ways customers phrase each answer, in every language, and the replies, recorded in advance.

5. 1:23

   Nothing goes live unchecked. Every change DAKSH proposes is validated, then approved by a person. And when it's done, DAKSH steps away. There is no DAKSH at run time. Only the workflow it wrote.

6. 1:37

   The floor is the VARTA cognitive engine. Seven layers, each with one job. A reflex layer, for instant replies. Recognition, for names, numbers and dates. Routing, to pick the next branch. Rules, to decide. A voice layer, to speak. And an observer, watching the whole call.

7. 1:59

   And the language model? It's one layer of seven, and it goes last. VARTA asks it only when the rules can't decide. Even then, it picks from the branches the workflow allows. It cannot invent a route.

8. 2:14

   Rules decide in under two milliseconds, and the reply is audio prepared in advance, in the same voice. In our Bank KYC replay, eleven of thirteen customer turns never touched a language model.

9. 2:28

   So what does that give a business? Speed: no awkward pauses. Consistency: the same question gets the same approved answer, every time. Control: the rules are readable, so compliance can review them before a single call goes out.

10. 2:46

    And cost. The model is paid once, to build the workflow, not on every turn of every call. Modelled with our cost estimator, LLM spend falls by ninety-six to ninety-nine percent.

11. 3:00

    A bank asks its KYC questions in the same approved order and wording, on every call. An insurer confirms a policy number by rule, not by guesswork. A service company books a technician, without the caller waiting on a model.

12. 3:16

    A prompt is a wish. Probabilistic AI is brilliant at understanding and writing language, and that's exactly where we use it: to build. But a business process needs the same input to give the same output, an answer in milliseconds, and a record of why. You don't get that by asking a model nicely. You get it by compiling.

13. 3:39

    DAKSH trains it once. VARTA runs it a million times. Fast, predictable, and built for business.