Enterprise Voice AI platform for Speech-to-Text, Text-to-Speech, and Voice Agents
Open-source AI agent orchestration platform for incident response and automation
Stage
Established
Not given
Founded
2015
Not given
Based in
US
Not given
Pricing model
Freemium
Open source
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/
Not given
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
Incident response with agent investigation and pull request generation
Automated workflows triggered on schedules or webhooks
Agent summoning from GitHub, GitLab and Radicle with /ryph command
Semantic memory system for shared and persistent agent learning
Credentials kept behind gateways with no access in sandbox
Customized environments per repository with cached dependencies
Browser use for agent interactions with live watching
Artifacts and pages for reports and overviews
Deployment options from laptop to Kubernetes
Tool proxy with allowlisted MCP server integrations
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
Agents work without holding real credentials or keys
Changes ship as patches that require review before merge
Runs on your own infrastructure from laptop to cloud
Semantic memory learns across runs with deduplication and contradiction handling
Built-in safety assuming agents can be manipulated or exploited
Deepgram
Ryph