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VARTA Cognitive Engine

LLM-last, not LLM-first

Most voice stacks send every word to a large model. VARTA classifies, caches and retrieves first, and reasons only when nothing cheaper can answer. That is why it is fast, affordable and resilient.

  • <500msEnd-to-end response
  • <120msCached turn latency
  • 62%Turns never touch an LLM
  • 9Pipeline layers

Architecture

Three zones, nine layers

Every layer has a latency budget and a fallback. Nothing in the pipeline can block a call.

Zone 1 — Perceive

  • Layer 1

    Streaming ASR

    Multilingual acoustic models tuned on Indian accents, with partial hypotheses emitted every 80ms.

  • Layer 2

    Language & code-switch detection

    Detects the caller's language per utterance, not per call, so Hinglish is handled natively.

  • Layer 3

    Intent & entity classification

    A fast classifier resolves the majority of turns without invoking any large model.

Zone 2 — Decide

  • Layer 4

    Response cache

    Semantic cache serves known turns in under 120ms at effectively zero marginal cost.

  • Layer 5

    Knowledge retrieval

    Company, department and flow-scoped retrieval grounds answers in your own documents.

  • Layer 6

    NITI policy & guardrails

    Stacked company, department and flow rules decide what the agent may say and do.

  • Layer 7

    LLM reasoning (last resort)

    Only genuinely novel turns reach a model — so you can afford a better one for them.

Zone 3 — Act

  • Layer 8

    Action & integration layer

    Defined action first, universal API call second, graceful degradation third.

  • Layer 9

    Neural TTS

    Natural Indic voices with cached audio reuse for repeated phrases.

Why it matters

What the architecture buys you

  • Latency you can feel

    A cached greeting or confirmation returns before the caller finishes exhaling. No awkward gap.

  • Cost that scales down

    At a 60% cache-hit rate, blended cost per call roughly halves versus an LLM-first stack.

  • Better model where it counts

    Because only hard turns reach the model, you can route them to a stronger, pricier one.

  • Graceful degradation

    If a provider degrades, cached and retrieved turns keep working. The call does not die.

  • Policy before generation

    NITI guardrails run before the model speaks, not as a post-hoc filter on its output.

  • Indic-first speech

    Recognition and synthesis tuned on Indian accents and code-switched speech, not adapted from English.

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