Intelligent Apps, Customized With Your Data
Deploy powerful AI applications, designed to be customized by your teams using your data to solve your unique needs.
Improve performance by exploiting features unique to your data with custom classification apps
Named Entity Recognition
Solve domain-specific syntactic and semantic challenges with precise NER apps
Collect useful text and data from virtually any table or form with flexible extraction apps
Knowledge Base Construction
Populate relational databases with information extracted from documents and structured sources.
Industry Use Cases
Fast Yet Accurate AI Automation
Build industry-specific AI applications combining state-of-the-art machine learning approaches with industry-specific best practices and last-mile connectors, all on an enterprise-scale platform.
- Contract Intelligence
- Anti-Money Laundering
- News Analytics
- Financial Spreading
- Condition Detection
- Drug Discovery
- Clinical Trial Matching
- Mental Health Support
- Claims Fraud Detection
- Automated Underwriting
- Customer Service
- Risk Evaluation
- Quality Assurance
- Social Media Analytics
- Brand Monitoring
- Product Recommendation
TELECOM & CYBER
- Intrusion Detection
- Network Optimization
- Customer Segmentation
- Price Optimization
- Customer Support
- Traffic Monitoring
- Email Filtering and Routing
- Invoice Processing
Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines.
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.
Google deployed early versions of Snorkel core technology with three high-impact teams, repurposing many resources as labeling functions.
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and acceleration of ML adoption.
of hand-labeling data replaced in 30 mins
hand labels replaced with
How Snorkel Flow Works
A Radically Different Approach to AI
Change how you annotate data, train and deploy models with Snorkel Flow, an end-to-end ML platform developed over five years at the Stanford AI lab. Based on research represented in over thirty peer-reviewed publications and deployed by some of the world's leading organizations such as Google, Intel, Stanford Medicine, and DARPA.
An End-to-end ML Platform
Designed for Collaboration
Data Scientist Friendly
- Integrated Jupyter notebooks
- Instant analysis tools
- Ready-to-use models
Domain Expert Friendly
- Intuitive, no-code UI
- Rich dashboards and visualizations
- Full-featured, push-button error analysis
- Platform access via Python SDK
- Online or batch API deployment
- Containerized software for cloud or on-premises deployments