No invented prices, dates or IDs
Logic, not the language model, owns anything that becomes a record. When AI runs, it chooses from a shortlist built by the workflow — never composes a number.
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Platform
A VARTA call moves through seven explicit layers — L1 to L7 — each with a narrow, inspectable job. Call flows are deterministic step graphs: the same input takes the same path, every time. Only turns that genuinely need reasoning reach an LLM.
Design principles
Logic, not the language model, owns anything that becomes a record. When AI runs, it chooses from a shortlist built by the workflow — never composes a number.
Most turns are resolved by logic before a model is called, fillers play instantly, and audio already generated is replayed rather than resynthesised.
The stable skeleton of a sentence is cached once; only slot values (name, amount, order number) are generated. Context is part of the cache identity.
Every model call, synthesised character and second of silence is priced and attributed to the call — a real per-call ledger.
No English default inside the engine. Per-language content lives in the workflow, including gender-agreeing Hinglish grammar and mid-sentence code-switching.
Repeat-back, revisit-and-invalidate, and side-question-then-return are handled by deterministic handlers, not model reasoning.
Named empathy scenarios with explicit can / cannot / must-never policies — e.g. explain pricing and offer a callback, never invent a discount.
Describe the call in plain English; the co-author drafts steps, rules, FAQs and language variants — every change validated before it applies.
TTS, STT and LLM each sit behind their own provider abstraction, selectable per workflow. Switching vendors is configuration, not migration.
An AI plays the customer across a scenario suite and diffs quality, per-layer latency and per-call cost before vs after every change.
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