The execution layer for what text destroys.
Your voice agent processes the transcript. 0-Lookback computes the intent.
Text is a transcript of intelligence. Not intelligence itself.
Every voice AI pipeline in production today follows the same architecture: Speech-to-Text, LLM, Text-to-Speech. The audio goes in. The transcript comes out. The LLM thinks on the transcript.
What the transcript does not contain: the 340ms hesitation before a decision. The rising intonation that marks a question the speaker hasn't finished asking. The backchannel — "right," "uh-huh," "mm" — that means keep going, not I'm done. The 600ms pause that means I'm thinking, not I've yielded the floor.
These variables are stripped at the STT stage. They are discarded as acoustic noise. The LLM never sees them.
This is why VAD-based voice agents interrupt users mid-thought. This is why they respond to backchannels as if they were turn yields. This is why they trigger LLM calls on input that was never meant for them — generating responses that are discarded, and API costs that are not.
VAD fires on two-thirds of backchannels. Every false interruption is a wasted LLM call. At scale, that is not a UX problem. It is a structural cost problem compounding with every conversation.
The industry knows the pipeline is broken. They are spending billions to make it run faster.
0-Lookback does not make the broken pipeline faster. It replaces the broken step.
The Behavioral ISA intercepts the raw audio stream before the STT cascade begins. It classifies the intent encoded in the audio — hesitation, pacing, intonation, pause duration — in real time, on Cerebras Wafer-Scale infrastructure. The behavioral classification travels alongside the transcript to the LLM. The model receives not just what was said, but what was meant.
One integration. Between the audio stream and the LLM.
Before the transcript.
The Behavioral ISA receives the raw audio stream upstream of your STT pipeline. The behavioral variables — hesitation timing, intonation gradient, pause duration — are intact.
Intent. Not volume.
The ISA runs on Cerebras Wafer-Scale infrastructure. 44GB of on-chip SRAM. Zero off-chip memory transfer. Each audio segment is classified in real time: thinking pause, turn yield, backchannel, interruption.
The transcript plus the intent.
The LLM receives two inputs: the standard transcript from your existing STT layer, and a structured behavioral classification from 0-Lookback. Your agent knows what was said. It also knows what was meant.
VAD is a volume gate. It answers one question: is there sound? 0-Lookback answers the question that matters: what does this sound mean?
One endpoint. Between audio and LLM.
0-Lookback runs as a preprocessing layer in your existing voice agent stack. It does not replace your STT provider. It sits between the raw audio stream and the transcription stage and returns a structured behavioral classification alongside every transcript segment.
{"segment_id": "seg_00421","transcript": "right, so what I was thinking was—","behavioral_classification": {"intent": "mid_utterance","pause_type": "thinking","confidence": 0.94,"recommended_action": "hold"}}
recommended_action: "hold" — the agent holds. The user finishes the thought.VAD was our biggest unsolved problem. 0-Lookback is the first thing that actually addresses the architecture, not just the threshold.
The behavioral signal is there. Your pipeline is discarding it.
Request access to the 0-Lookback API. Early access is open to production voice agent developers.
Developer tier: $500/month. Production tier: $2,000/month. Usage-based overage above tier limits.