Snorkel started as an academic project that asked whether people could label data programmatically. Instead of tagging thousands of examples one by one, a domain expert writes labeling functions, which are rules, patterns, or model calls that vote on a label. Snorkel combines their noisy outputs into training labels, a technique called weak supervision.
The company has moved toward the needs of generative AI. Its platform helps enterprises curate data for fine-tuning and evaluating language models, with experts reviewing samples and defining what good answers look like. A network of expert annotators supplies specialized data in areas like law, medicine, and finance, and tools measure how models perform on custom benchmarks.
Snorkel sells to large organizations and AI labs on enterprise contracts. The approach saves labeling time but still needs domain experts, and quality depends on how well the rules reflect reality. Alternatives include Scale AI, Surge AI, Labelbox, Argilla, and Label Studio for in-house labeling. Snorkel AI suits teams that need specialized training and evaluation data at scale.