About

Mira is a data annotation studio by HyperneuronAI, built around a simple belief: a model is only as trustworthy as the data it was trained on. Not all data is great data, and the gap between the two is where most models quietly fail. Mira exists to close that gap before the data ever reaches training.

We started in speech — transcription, diarisation, prosody, preference — and grew into the full multimodal stack: text, audio and image under one workflow. The same dataset can be labelled for an ASR pipeline, scored for a TTS voice, and ranked for an alignment run, without being re-handed between three teams that each keep their own version of the truth.

Every judgement we produce carries its evidence. A score is never a single number floating free of the sample that produced it; a transcript is never just the final text without the passes and the adjudication notes behind it. The raw audio, the references and the per-slice breakdowns travel with every label, so a training or product team can open any cell and see exactly which examples broke and why.

We work the way a lab works, not the way a marketplace does. Panels are calibrated before they touch your data, disagreements are escalated and resolved rather than averaged away, and the style guide is yours, not ours. The result is data you can audit line by line, built to the same standard whether the batch is a thousand clips or a million.

Mira is for teams that have stopped trusting benchmarks they cannot reproduce. We give them the reference data, the evaluation runs and the annotation layer underneath — versioned, slice-aware, and attached to the evidence — so the next model they ship is built on data they actually understand.