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Building intelligence
that can listen.
AdofLabs is building real time intelligence that forms an evolving understanding of the world, adapts as it changes, and turns intent into verified action.
AdofLabsstartedwithonequestion:howdowebuildintelligencethatmaintainsanevolvingunderstandingoftheworldwhileperceiving,reasoningandactingcontinuouslyatrealworldspeed,withinpracticalcompute,andtowardverifiableoutcomes?
We’re developing architectures for intelligence that can remain continuously aware and act in real time.
Instead of forcing perception, memory, reasoning, tool use and verification through one expensive loop, we’re exploring how they can operate concurrently at the speed and compute each requires, while sharing an evolving understanding of the world. The goal is intelligence that stays responsive, revises its understanding as conditions change, and can act toward verifiable outcomes.
Our Progress
We are working in the open. Here is a look at what we are building, measuring, and testing right now.
APPLIED RESEARCH + EVALS
We’re studying current real-time systems and building our own evaluations around response timing, interruptions, naturalness, state continuity, tool completion and inference cost.
REALTIME BASELINE A1
Built and instrumented the first live audio transport path.
320 ms → ~100–121 ms Observed round-trip latency across successive runs.
REALTIME BASELINE A2
Added speech understanding to the same measured pipeline.
The first integration exposed an empty-transcription failure. We’re isolating it before adding another layer.
Our Progress
We are working in the open. Here is a look at what we are building, measuring, and testing right now.
APPLIED RESEARCH + EVALS
We’re studying current real-time systems and building our own evaluations around response timing, interruptions, naturalness, state continuity, tool completion and inference cost.
REALTIME BASELINE A1
Built and instrumented the first live audio transport path.
320 ms → ~100–121 ms Observed round-trip latency across successive runs.
REALTIME BASELINE A2
Added speech understanding to the same measured pipeline.
The first integration exposed an empty-transcription failure. We’re isolating it before adding another layer.
Built, tested, and released from our research.
Reports, experiments, demos and systems that make our work inspectable.
Measuring the real-time speech stack, layer by layer.
An instrumented baseline of browser capture, encoded audio transport and the first speech-recognition integration.
A1 / VALIDATED
84–105 msobserved application RTT17.9–25.9 msmeasured server processingA2-A / INCOMPLETE
Keeping interaction alive while actions execute.
We’re testing whether speech, state updates and tool execution can run concurrently instead of forcing the system into a sequential listen → reason → act → wait → respond pipeline. The goal is to keep the interaction responsive while work continues in the background, without losing state or execution context.
