Enterprise Voice AI platform for Speech-to-Text, Text-to-Speech, and Voice Agents
Memory that reasons
Stage
Established
Not given
Founded
2015
Not given
Based in
US
US
Pricing model
Freemium
Freemium
Pricing
Flux STT Streaming: $0.0065/min (Pay As You Go), $0.0057/min (Growth); Flux Multilingual Streaming: $0.0078/min (Pay As You Go), $0.0068/min (Growth); Nova-3 Monolingual Streaming: $0.0048/min (Pay As You Go), $0.0042/min (Growth); Nova-3 Multilingual Streaming: $0.0058/min (Pay As You Go), $0.0050/
Free plan at $0 USD; Pro plan at $5 USD per month.
Key features
Flux STT with turn detection and interruption handling
Nova-3 multilingual speech recognition in 50+ languages
Ultra-low latency under 300 milliseconds
Keyterm prompting with up to 90% higher keyword recall
Flux TTS with latency as low as 80ms
Speaker diarization and multi-speaker audio detection
Smart formatting with automatic punctuation and capitalization
Automatic redaction of sensitive information
Custom models for edge-case scenarios
Flexible deployment options: cloud, in-VPC, or self-hosted
Entity-graph spreading activation for cross-vocabulary retrieval
Works with any LLM model
No LLM or GPU in retrieval path
Point-in-time and windowed recall
Timestamped memories
Fixed recall cost as memory grows
What makes it different
Conversation-aware speech platform with native turn-taking detection and interruption handling
Unified single API combining STT, TTS, and LLM orchestration to reduce complexity and latency
Flux TTS that thinks before speaking to maintain tone and expression across conversations
Enterprise-grade accuracy with custom vocabulary injection and industry-tuned models
Foundational AI company with research-driven innovation and continuous model improvements
Reaches answers across unrelated documents that share no keywords, where vector search scores zero
Deterministic retrieval with no model in the path, so results never vary between queries
Constant per-query cost regardless of memory size, even at 32,000 words
No infrastructure to run—no vector database, cluster, or GPU needed
Works with any model, so small local models gain the most from better retrieval
Deepgram