Digital Pathology & AI
Consistent slides make consistent models
In development — not yet a product
A model trained on slides is a model trained on whatever variation those slides carry. Batch effects, operator differences and drifting staining conditions all end up encoded as signal, and no amount of architecture compensates for it.
That makes reproducible sample preparation a prerequisite for digital pathology AI rather than a convenience alongside it. It is the part of the pipeline we already automate, and it is why this is the direction we are heading.
What we are working towards
Instruments that produce training-grade consistency by construction, with the run conditions behind every slide recorded rather than inferred — so a dataset can be audited as well as used.
We are not announcing a product or a date. If this is a problem you are working on, we would rather hear about it early than present you with something finished and wrong.