Information extraction

Rapidly build AI-powered applications that extract information from unstructured text, PDF, tables, or forms from millions of documents with programmatic labeling using Snorkel Flow.

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Data-centric AI technology developed at the Stanford AI Lab and proven at world-leading companies.

How Snorkel Flow works

Targeted applications to tackle any entity

Extract useful data from any tables, cells, and forms linked to all headers, units, or references.
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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.
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Higher-accuracy models

Iterate on your application, using a closed-loop approach with intermediate results and analysis at every step to zero in on errors.
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Flexible integrations

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.
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Easier SME collaboration

Build complex classification apps intuitively while preserving natural information about data taxonomies with subject matter expert (SME) collaboration.

Information extraction

Programmatically label training data across complex data types and build multi-model information extraction applications with ease.
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An end-to-end ML platform

Designed for collaboration

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For data scientists

  • Ready-to-use model zoo
  • Auto-generated analysis tools
  • Integrated Python notebooks
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For domain experts

  • Rich data annotation suite
  • Intuitive, no-code labeling UI
  • Model error analysis reports
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For developers

  • Fully interoperable API and web UI
  • Write custom operators with Python SDK
  • Integrations to deploy models at scale
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Case study

Top U.S. bank

A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their documents.
Read more



Problem

The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.

Solution

With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.

Results

The resulting AI application could be quickly and easily adapted to new problems and business lines.

99.1%

Snorkel Flow accuracy

<24hrs

from problem start

>250K

documents processed


Case Study

Top U.S. bank

A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their documents.
Read more



Problem

The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.

99.1%

Snorkel Flow accuracy

Solution

With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.

<24hrs

from problem start

Results

The resulting AI application could be quickly and easily adapted to new problems and business lines.

>250K

documents processed


Case Study

Top U.S. bank

A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their documents.
Read More



Problem

The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.

Solution

With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.

Results

The resulting AI application could be quickly and easily adapted to new problems and business lines.

99.1%

Snorkel Flow accuracy

<24hrs

from problem start

>250K

documents processed


Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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September 20, 2021
Snorkel AI welcomes industry leaders to the team

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August 9, 2021
This hot startup is now valued at $1 billion for its A.I. skills

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February 24, 2021
The Data-First Enterprise AI Revolution

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July 14, 2020
Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning

Blog
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February 4, 2022
Making Automated Data Labeling a Reality in Modern AI

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Date: Jan 25, 2022
The Principles of Data-Centric AI Development

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Date: Jan 5, 2022
Meet the Snorkelers

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Date: Jul 9, 2021
How to Use Snorkel to Build AI Applications

Research
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2022
Universalizing Weak Supervision

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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records

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2017
Rapid Training Data Creation with Weak Supervision

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2016
Data Programming: Creating Large Datasets Quickly

Customer Stories
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February 26, 2022
Genentech used Snorkel Flow to extract information from clinical trials

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February 18, 2022
Google used Snorkel to build and adapt content classification models

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2019
Intel used Snorkel to accelerate sales and marketing agents

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2019
Apple built a Snorkel-based system to answer billions of queries in multiple languages

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Let’s connect

Speed time to value, reduce costs, and unlock more AI possibility 
with the Snorkel Flow platform.
Request a demo