Giving machines human-level
audio and speech perception.

We've been developing voice technologies for consumer electronics for 8 years. Before the era of ElevenLabs, or even OpenAI. We realized early on that all the leading speech models (at the time, Google and Microsoft) were optimized for the "digital" realm: transcripts for call centers, meeting notes for Zoom calls, captions for podcasts and movies. As soon as you deploy them in the "messy" physical world—far-field, reverberant, low SNR, overlapping speech—they degrade rapidly.

Part of the problem is the scarcity of real-world, noisy audio data. The largest datasets and evaluation benchmarks for speech models are not even close to the complexity of the real world.

A sample from Librispeech, the de-facto reference benchmark for ASR. Carefully segmented, clean, single-speaker audiobook speech.

Real-world sample from our proprietary dataset. Background noise, distance, interfering speakers all compete with the target voice.

The gap remains surprisingly wide - Ayllon et al. (2026) evaluated leading 40 ASR models and found that median word error rate (WER) reached 20.45% on speech with background noise - the same models report 2-3% on clean benchmarks. This study also found evidence of optimization for benchmarks ("benchmark-maxxing").^[1]

Speech recognition ("ASR" or "STT") answers one question: what words were spoken? Simply slapping an STT model onto a machine does not make it good at comprehending real-world audio. A useful system must also infer who spoke, where the voice is, whether it was directed at the device, which speaker to focus on, all while enhancing speech and suppressing interfering noise. This is known as the "cocktail-party problem." Humans can do this in a few milliseconds. Machines have not been able to do this well, despite efforts since 1953^[2].

We don't just develop STT models. We also build dedicated models for:

• Target speaker extraction
• Directional speech enhancement
• Speaker diarization
• Speaker identification
• Sound source localization (SSL)
• Continuous moving speaker tracking
• Overlapping speech separation
• Speech enhancement and dereverberation

Human-machine interaction cannot wait for an upload. A real-time system must be causal: at time t, it can use only samples at or before t. It never sees future context that would make the current decision easier. Thus, batch (async / offline) model accuracy therefore overstates what an interactive (streaming) system can do.

Causality changes architecture, training, and evaluation. Every stage must keep bounded state, recover continuously, and process audio faster than it arrives (a real-time factor "RTF" below 1.0) to keep end-to-end latency low enough for natural turn-taking.

Intelligent machines should not lose the ability to communicate when they lose internet. Your car's voice assistant shouldn't stop working in a tunnel; voice dictation on a smartphone shouldn't be handicapped when you're on a hike. Nor should every spoken interaction incur a metered charge per word.

Edge intelligence used to lose on two fronts: devices lacked compute, and capable models were too large. Both constraints are moving quickly. Apple's A19 Pro (iPhone 17 Pro) combines a 16-core Neural Engine with Neural Accelerators in every GPU core and reports up to 40% better sustained performance than its predecessor. Novel specialized audio model architectures concentrate capacity on one task, delivering high intelligence density where the microphones are. Recent wave-based simulation methods can generate controlled mixtures of rooms, motion, reverberation, noise, and overlapping speakers, leading to higher quality training data.

All our models are optimized for edge deployments on consumer grade devices (modern smartphones, laptops, wearable NPUs) while maintaining RTFs < 1. This enables private, low-latency, and cost-effective voice AI that works everywhere, anytime.