AI for insurance

From improving claims experience to fraud detection, Snorkel Flow provides insurance innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.

Request a demo

AI for insurance

From improving claims experience to fraud detection, Snorkel Flow provides insurance innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.

Request a demo

Data-centric AI

Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
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Accelerated

Save time and costs by replacing manual labeling with rapid, programmatic labeling.
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Adaptable

Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
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Collaborative

Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
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Accurate

Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
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Governable

Version and audit data like code, leading to more responsive and ethical deployments.
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Private

Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.

A radically new approach to AI

Conventional AI approaches rely on generic third-party models, or brittle rule-based systems, or armies of human labelers. With Snorkel Flow, programmatically labeling unlocks a new workflow that accelerates AI app development.

With Snorkel Flow

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Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
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Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
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Label thousands of data points programmatically in hours while keeping your data in-house and private.

With conventional approaches

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Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
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Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
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Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.

The platform for data-centric AI development

The platform for data-centric AI development

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