Snorkel AI builds the training data, benchmarks and evaluation environments that frontier models need to work in specialist domains. Its framing is precise about where the problem lies: frontier models break at the edges, and the edges are exactly where regulated and high-stakes work happens.
That is a real and under-served gap. A general model performs well on common tasks and degrades on specialist ones, because the relevant expertise is scarce in training data by definition. Building domain-specific data and, crucially, the evaluation environments to measure whether performance actually improved is what turns a promising model into a deployable one.
Snorkel has a strong research lineage in programmatic data labelling, which is the technique that made this approach viable at scale rather than requiring armies of expert annotators. The evaluation half deserves as much attention as the data half: without a domain benchmark you cannot tell whether a change helped.
