The problem
Most conversational AI arrives too late or speaks at the wrong time. A summary after the meeting cannot change the meeting. A useful live system must recognise words — and also hold context, judge timing and stay out of the way.
The central idea
Treat a conversation as a stream with state. Context is kept across turns and interruptions; work is routed to the model whose latency fits the moment; assistance is timed to the conversation, not to the model.
How it works
- Streaming speech
- Continuous transcription and speaker separation while the conversation happens.
- Conversational state
- A running model of the conversation — topics, claims, open questions — that persists across turns and interruptions.
- Temporal channels
- Parallel lines of reasoning, each with its own purpose and cadence, working alongside the conversation without blocking it.
- Latency-aware routing
- Fast checks stay close to the stream; deeper analysis goes to larger models or external sources when time allows.
Capabilities
- Streaming speech interaction
- Conversational state management
- Temporal context across turns
- Context-aware, well-timed assistance
- Multimodal interaction
- Voice-driven workflows
- Low-latency system design
Where it operates
Interviews, technical discussions, advisory and sales conversations — settings where the right fact or question is only valuable during the conversation.
Engineering considerations
Latency is a budget allocated across the pipeline, not a property of one model. Consent and data handling are configured per deployment. The person in the conversation stays in charge of what is said.
Concepts
Within Equations Work
A proprietary Equations Work product, and the basis for our real-time voice and conversational engagements.
ah + ware = aware.