case study NUYDA

Nuyda — Voice-Powered RAG AI System

• LLMs hallucinate — they make up answers when they don’t know something, producing untrustworthy responses and factual errors.
• LLMs have a knowledge cutoff — they can’t answer questions about recent or private domain documents.
• Speech adds latency — ASR + LLM + TTS each add 200–800ms, pushing total latency past 2–3 seconds and breaking natural conversation.
• ASR errors propagate — misheard words become wrong queries, so retrieval fails and the LLM gets bad context.
• Vector DB queries are slow — 50–300ms per retrieval blows the 200ms budget for natural voice flow.
• No safety guardrails — LLMs can output harmful or PII content, creating compliance risk and user harm.
• Cloud-only isn’t always viable — privacy, offline, and cost concerns block deployment in sensitive environments.

• Wrapped RAG in a voice interface: speech-to-text → query embedding → retrieve + rerank → LLM with safety filter → streaming text-to-speech.
• Two-pass endpointing triggers RAG on the 240ms interim transcript instead of waiting 800ms for the final transcript — saving ~560ms of perceived latency.
• Predictive semantic cache pre-fetches likely follow-up chunks in the background (Slow Thinker / Fast Talker dual-agent) for sub-millisecond retrieval on cache hits.
• Streaming TTS speaks LLM tokens as they arrive (110ms first-token-to-speech) with barge-in so users can interrupt mid-response.
• Layered safety: input filtering before RAG plus output filtering with PII detection across 23 categories and 20+ languages — deployable cloud, on-device, or hybrid.