About
Purpose-built data for specific model problems.
Byser designs and delivers datasets for AI training and evaluation.
The file is only part of the dataset.
What makes data useful to a model is the structure around it: what one example represents, how the source media connects to the target, which metadata matters and where the supervision came from. Byser focuses on those relationships from the start.
Technical detail with a purpose.
A transcript matters when it provides the target for speech. OCR matters when visible text is part of the model input. Temporal segments matter when a long recording contains several useful events. Manifests and stable identifiers matter when technical teams need to join those elements reliably. We use structure where it makes the data more intelligible and more useful to the task.
Data that represents the context.
AI systems should be useful in the human contexts they are built to support, and that begins with data that represents those contexts faithfully.