Snorkel has tackled one of the most persistent bottlenecks in AI development: the creation of high-quality labelled data. While the broader industry continues to chase model performance and novel architectures, Snorkel focused on the less visible but critically foundational layer that makes those models work in the real world. Through its flagship product, Snorkel Flow, Snorkel AI has redefined the economics and speed of training data generation, enabling organizations to build domain-specific AI systems faster, more accurately, and with significantly lower overhead.
As LLMs become increasingly commoditized, the differentiator for most enterprises is not the model but the data. Enterprises operate in complex, regulated, and idiosyncratic environments where generic datasets and pre-trained models fall short. Traditional data labelling approaches are slow, costly, and unsuited to domains where expert input is expensive or rare. Snorkel Flow solves this by introducing programmatic data labelling, replacing manual annotation with rule-based, automated processes that are auditable, iterative, up to 100 times faster.
Snorkel Flow empowers organizations to curate datasets using heuristics and domain rules. These methods generate labelled data that is quicker to produce and often higher quality than traditional annotation pipelines. It enables the development of AI that is tailored, not just generic applications tweaked for enterprise use, but models built from the ground up with the organization’s unique data context.
Its complement, Snorkel Custom, accelerates the delivery of production-quality AI systems, such as copilots and chatbots that blend LLM performance with sector-specific knowledge. Together, they offer a pipeline that spans data creation to deployment, creating a full-stack enabler for enterprise AI.
Snorkel’s impact is not theoretical. It is already in use at scale across critical sectors:
- Healthcare: At Memorial Sloan Kettering and Stanford Medicine, Snorkel slashed manual labelling timelines from years to hours freeing clinical staff to focus on care rather than annotation.
- Finance: Top-tier US banks have achieved leaps in model accuracy, from 25% to over 90% in two months, by using Snorkel with GPT-4 and retrieval-augmented generation. Another bank automated the labelling of 250,000 legal documents, achieving 99.1% model accuracy and saving months of SME time.
- Retail: Wayfair accelerated product tagging tenfold across 40 million items, improving discoverability and customer experience.
- Insurance and Pharma: Organizations reported more than $150,000 in savings, thousands of high-quality labels, and 35-fold improvements in model development speed.
- Telecoms and BPO: Snorkel improved customer service accuracy and enabled a 20-times faster time-to-value in model deployment.
Snorkel enhances productivity and shifts the AI development paradigm. By enabling domain experts to guide model training without needing to code or manually annotate at scale, Snorkel decentralizes AI creation and empowers the very people who understand the problems to shape the solutions.
