What Makes Opal Different
No ASR Pipeline
Direct speech processing means faster responses and better context awareness. No transcription errors to compound.
Ultra-Low Latency
Sub-300ms first byte latency enables natural turn-taking that feels human, not robotic.
High Intelligence
Advanced reasoning with complex instruction following, context retention, and task completion.
Emotion Awareness
Understands emotional cues and responds with appropriate tone and inflection.
Core Architecture
Unlike traditional voice AI that chains separate ASR, LLM, and TTS components, Opal understands speech directly. This eliminates latency penalties and error propagation between stages.
Traditional Cascaded Approach
Each stage introduces latency. Transcription errors compound through the pipeline. Total response time is the sum of all components.Opal End-to-End Approach
Opal supports two output modes depending on your use case:- Full Speech-to-Speech
- Speech-to-Text
The model operates entirely in embedding space, preserving acoustic information that would be lost in text-based intermediate representations. No transcription step means no transcription errors.
Performance Comparison
| Metric | Opal | Traditional Cascade |
|---|---|---|
| First byte latency | Under 300ms | 800-1500ms |
| Turn-taking gap | Natural | Noticeable delay |
| Interruption handling | Native | Often problematic |
| Error propagation | None | Compounds across stages |
Key Capabilities
High Intelligence & Instruction Following
High Intelligence & Instruction Following
Opal combines speech understanding with advanced reasoning:
- Complex instruction following — Handles multi-step requests and nuanced instructions
- Context retention — Maintains conversation context across long interactions
- Domain adaptation — Quickly adapts to specialized terminology and workflows
- Task completion — Drives conversations toward defined goals while handling edge cases
Voice Preservation & Custom Voices
Voice Preservation & Custom Voices
Opal maintains consistent voice characteristics throughout conversations or adopts custom voice profiles. This enables branded voice experiences that match your organization’s identity.
Emotion & Tone Awareness
Emotion & Tone Awareness
The model understands emotional cues in speech and responds appropriately:
- Detecting caller frustration, confusion, or satisfaction
- Adjusting response tone to match the situation
- Conveying empathy, urgency, or reassurance as needed
Multilingual Support
Multilingual Support
Opal supports multiple languages and accents, enabling global deployment without requiring separate models for each locale.
Streaming Inference
Streaming Inference
Opal supports streaming in both directions:
- Input streaming — Begins processing before the speaker finishes
- Output streaming — Starts speaking while still generating the response