Top US Bank
accuracy for contract intelligence built using Snorkel Flow in under 24 hours
Extract useful data from any tables, cells, and forms linked to all headers, units, or references.
Faster, Lower-cost Development
Use programmatic labeling to develop high-quality AI applications in hours instead of spending weeks or months on expensive hand-labeling.
Monitor for changes in the data, and rapidly adapt using built-in error analysis tools. Zoom in on errors to fine-tune training data & models with guided iteration.
Leverage large amounts of labeled and unlabeled data, NLP primitives, and state-of-the-art model architectures to build high-accuracy models.
Easily integrate labeling, training, and analysis pipelines defined over diverse input types–text, PDF, HTML, and more–with downstream applications using APIs or a Python SDK.
Information Extraction Customized for Your Workflow
Banks can extract information from financial reports to explore market trends.
TELECOM & CYBER
Network operators can extract information device details from technical documents to optimize network resources.
Pharmaceutical companies can automate data extraction from clinical trial records for digital pathology.
Insurance firms can evaluate risks associated with a policy by extracting contextual data from contracts and forms.
Software companies can extract information from invoices or receipts for accounting or expense analysis.
Online marketplaces can extract product attributes from tables, lists, and forms for cataloging.
Top US Bank
A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from contracts and other legal documents.
The bank estimated that, for a time-sensitive use case, labeling data by hand would take over a month.
With Snorkel Flow, the team produced a AI-powered contract intelligence application that was over 99% accurate in under 24 hours.
The resulting AI application was quickly and easily adapted to new problems.
to develop first model
How Snorkel Flow Works
Build flexible extraction applications that preserve structural and tabular information and generalize beyond brittle rules. Eliminate hundreds of hours of manual labor by programmatically labeling data with powerful labeling functions. Train state-of-the-art models and analyze performance using intuitive tools. Chain ML tasks together and deploy your extraction application as an API or Python SDK with one click.
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
The Role of Massively Multi-Task and Weak Supervision in Software 2.0A. Ratner, et al, CIDR 2019
Snorkel: Fast Training Set Generation for Information ExtractionA. Ratner, et al, SIGMOD 2017
Learning to Compose Domain-Specific Transformations for Data AugmentationA. Ratner, et al, NeurIPS 2017