Snorkel AI Bet on Expert Data, But Can They Keep Up?

“Building expert-level agentic AI requires expert-level data” - Alex Ratner, CEO at Snorkel AI

One of the biggest hurdles Gen AI enterprises face is ensuring models can be trusted in real-world, specialized settings. That’s the challenge Snorkel AI was founded to solve in 2019. By shifting focus from model development to data development, Snorkel aimed to address the lack of high-quality, domain-specific training and evaluation data. Now, competitors such as Labelbox and Scale have seized the moment to position their platforms as end-to-end solutions. In an increasingly crowded market, Snorkel’s bet on expert-guided, data centric AI is in question.

The Palo Alto-based startup just announced a $100 million Series D round, bringing its total funding to $237 million and its valuation to $1.3 billion. Led by Addition, with participation from Greylock, Lightspeed, QBE Ventures, and others, the new funding will support further expansion of its flagship data development platform, Snorkel Flow, and the launch of new products aimed at strengthening enterprise AI evaluation.

Snorkel began in what co-founder and CEO Alex Ratner describes as an “afternoon project” at the Stanford AI Lab with Chris Re, Paroma Varma, Braden Hancock, and Henry Ehrenberg. Back then, ML teams were spending their time building complex models and treating training data as an afterthought. But Ratner and his co-founders saw that the real bottleneck was data, or rather, the effort required to label it for use in training. By 2019, Snorkel officially spun out of Stanford to commercialize a new approach called programmatic data labeling.