Backed by Y Combinator

We make AI understand you.

We are a leading data research lab on human interaction powering the next generation of speech and omni-models.

Our Mission

Diverse and complex human interactions enabled us to land on the moon. Interaction is much more than handling end-of-turns and barge-ins.

Our mission is to enable the most emotionally intelligent AI models that unlock the next level of human-AI interaction.

What are the best conversations you've had recently? That's the bar we're aiming for.

Our Research

We publish original research and open source our datasets on production failures we see in voice systems today.

EvaluationSeptember 10, 2026

Duplex Cue

An audio benchmark for measuring how an ongoing speaker responds when a listener contributes during the turn—whether the speaker continues unchanged, adapts in-turn, or yields the floor.

Duplex Cue assigns two independent labels to each local interaction. Cue intent describes Speaker B's contribution. Observed response describes Speaker A's behavior.

Fine-tuningAugust 28, 2026

Inkling Post-Training

We post-trained Thinking Machines' Inkling on selections of 1, 25, and 100 hours of VoiceCodeBench-style speech.

Our training goal was not to teach Inkling a lookup table of canonical values. It was to make the model better at hearing the acoustic structure that makes those values recoverable.

BenchmarkJuly 7, 2026

Vocal Affect Bench

A public, test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw speech — without transcripts or contextual metadata.

A customer who says "that's fine" may be conceding, joking, or signaling frustration. Automatic speech recognition preserves the phrase but strips the vocal delivery that makes the exchange interpretable.

BenchmarkMay 1, 2026

Voice Code Bench

WER measures transcript similarity. Production voice interfaces need something stricter: the exact email, command, account identifier, URL, date, file path, dollar amount, product code, or measurement a downstream system must act on.

Voice Code Bench is intentionally narrower than universal audio suites. It asks one product-critical question: did the transcript preserve every structured entity a downstream workflow needs?

EvaluationSeptember 10, 2026

Duplex Cue

An audio benchmark for measuring how an ongoing speaker responds when a listener contributes during the turn—whether the speaker continues unchanged, adapts in-turn, or yields the floor.

Duplex Cue assigns two independent labels to each local interaction. Cue intent describes Speaker B's contribution. Observed response describes Speaker A's behavior.

Fine-tuningAugust 28, 2026

Inkling Post-Training

We post-trained Thinking Machines' Inkling on selections of 1, 25, and 100 hours of VoiceCodeBench-style speech.

Our training goal was not to teach Inkling a lookup table of canonical values. It was to make the model better at hearing the acoustic structure that makes those values recoverable.

BenchmarkJuly 7, 2026

Vocal Affect Bench

A public, test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw speech — without transcripts or contextual metadata.

BenchmarkMay 1, 2026

Voice Code Bench

Voice Code Bench is intentionally narrower than universal audio suites. It asks one product-critical question: did the transcript preserve every structured entity a downstream workflow needs?

EvaluationSeptember 10, 2026

Duplex Cue

An audio benchmark for measuring how an ongoing speaker responds when a listener contributes during the turn—whether the speaker continues unchanged, adapts in-turn, or yields the floor.

Duplex Cue assigns two independent labels to each local interaction. Cue intent describes Speaker B's contribution. Observed response describes Speaker A's behavior.

Fine-tuningAugust 28, 2026

Inkling Post-Training

We post-trained Thinking Machines' Inkling on selections of 1, 25, and 100 hours of VoiceCodeBench-style speech.

Our training goal was not to teach Inkling a lookup table of canonical values. It was to make the model better at hearing the acoustic structure that makes those values recoverable.

BenchmarkJuly 7, 2026

Vocal Affect Bench

A public, test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw speech — without transcripts or contextual metadata.

BenchmarkMay 1, 2026

Voice Code Bench

Voice Code Bench is intentionally narrower than universal audio suites. It asks one product-critical question: did the transcript preserve every structured entity a downstream workflow needs?