Everyone evaluating AI avatar platforms focuses on voice quality. The bigger UX killer is almost always latency — and it's a harder problem than picking a good TTS provider.
Where the delay actually comes from:
User speaks/types
→ STT (if voice input) → LLM generates response (streaming helps, but first-token latency matters)
→ TTS converts text to audio
→ Audio playback + lip-sync rendering
Each hop adds latency. A naive implementation that waits for the full LLM response before starting TTS can easily hit 2-4 seconds of dead air — long enough for a user to assume the bot is broken.
How production systems actually solve this:
Token streaming into TTS — start synthesizing audio on partial LLM output (sentence-by-sentence chunks) instead of waiting for the full response
Speculative rendering — start lip-sync animation slightly ahead of audio using predicted phoneme timing
WebSocket/SSE persistent connections — avoid the overhead of repeated HTTP round-trips per turn
Regional API routing — TTS/LLM provider latency varies a lot by user geography; this matters more than most benchmarks show
A practical note: platforms that advertise "real-time" avatars but load all logic behind a single request/response cycle will feel noticeably worse than ones built around streaming pipelines, even if they use the identical LLM and TTS providers underneath. If you're evaluating a platform (or building one), test with realistic network conditions, not office wifi — that's where the architecture differences actually show up.
Bottom line: the voice provider matters less than people think. The orchestration around it — how aggressively you stream and pipeline each stage — is what separates a "wow" demo from a production-ready conversational agent.