Two different jobs, and we do both.

Building the model. Traditional supervised learning and deep learning applied to your data, including prediction with pretrained biological models such as GeneFormer and Akita.

Checking the model, and the code. A model built for a different platform or population can underperform on yours while looking just as confident, and an LLM assistant will happily produce code that runs without doing the analysis you meant. We will tell you whether an approach fits your data, where a method's limitations bite, and what AI-generated code is actually computing.

The second job is the one researchers most often skip and most often need. It is also the one that is hardest to do alone, because the failure mode is a plausible answer rather than an error message.