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Speech-to-Speech models deliver natural, low-latency conversations, but enterprise Voice AI needs more than natural speech. Here’s where S2S fits—and where it doesn’t.
VARTA Engineering · · 8 min read
Voice AI can have low technical latency and still feel slow. Learn how turn-taking, acknowledgements, streaming and interruptions shape experiential latency.
VARTA Engineering · · 6 min read
Use Voice AI to automate service confirmation, scheduling, CSAT, AMC renewals and follow-ups while integrating directly with CRM, FSM and ERP systems.
VARTA Engineering · · 4 min read
LLM-first agents are flexible, but deterministic workflows offer control and predictability. See how each architecture behaves in production Voice AI.
VARTA Engineering · · 6 min read
Building Voice AI in-house makes sense. Rebuilding STT, LLM, TTS, streaming and orchestration may not. Build your agent on a production-ready voice runtime.
VARTA Engineering · · 7 min read
Voice AI cost is not one API price. It comes from telephony, STT, LLMs, TTS, tools and runtime overhead. Here’s how to engineer for lower cost.
VARTA Engineering · · 7 min read
Not every voice turn needs AI reasoning. Learn how deterministic routing, structured state and selective LLM calls can reduce latency, cost and unpredictability.
VARTA Engineering · · 7 min read
Voice AI latency comes from the whole runtime—not just the LLM. Learn how endpointing, STT, routing, APIs, TTS and streaming shape response time.
VARTA Engineering · · 6 min read
Voice AI demos sound natural, but production brings latency, hallucinations, workflow complexity, multilingual challenges, cost, and reliability issues.
VARTA Engineering · · 9 min read
A cached reply costs nothing and plays in milliseconds, but voice is hostile to naive caching. Why hashing text plateaus, how it fails in production, and what intelligent caching does instead.
VARTA Engineering · · 13 min read