A decision layer.
For speech.

Accuracy is contextual.

ASR performance changes across speakers, languages and recording conditions. Bessel evaluates candidate transcripts against the recording and returns a ranked comparison.

Ready for your recordings.

Your audio keeps changing. Bessel makes comparisons when you submit a recording, without requiring you to train it on your customer data first.

Bring transcripts from the models you already use, multiple decoding attempts or an updated model. Each candidate is judged against the same audio; its source label does not determine its quality.

A decision layer for your application.

Use candidate IDs and relative scores in your own selection workflow. When the evidence supports a recommendation, Bessel identifies the candidate. Otherwise it returns equivalent or insufficient_evidence so your code can retain an existing result or request review.

Rankings compare the candidates you supply. They do not certify absolute accuracy, and even the first candidate can contain errors. Scores are not calibrated probabilities. Choosing a stronger transcript can reduce word error rate; measuring that improvement requires a reference transcript.