Conversational AI

Build high-quality NLP and conversational AI applications by training state-of-the-art models with your data using Snorkel Flow.

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Deliver engaging experiences

With Snorkel Flow, you can build powerful conversational AI capabilities to understand customer intent, model topics, and analyze sentiment at an utterance or conversation level. Achieve higher quality with purpose-built models trained on your data–no costly hand-labeling needed.

High-accuracy models

Train state-of-the-art NLP models from Snorkel Flow’s built-in model zoo, or custom models via Python SDK with push-button UI. Deploy language models that take advantage of all previous utterances.
Customer: I need to transfer 1250 dollars [TRANSFER_MONEY]
Agent: Which account do you want to withdraw funds from?
Customer: Checking [FROM_ACCOUNT]
Agent: Please confirm your 4-digit passcode
Customer: 1132 [PASS_CODE]

Faster development

Accelerate development with template-driven visual builders for many common conversational use cases and a wide range of labeling functions and NLP tools.

Adaptable applications

Monitor performance drifts in Labeling Functions or the purpose-built models. Rapidly adapt to changes in data or business objectives without relabeling from scratch.

Collaborative workflows

Put customer service agents' or other customer experience experts' knowledge to use with a no-code, push-button UI to create labeling functions.

Data-centric AI

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


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


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


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


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


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


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

Case study

IBM bootstrapped chatbots with weak supervision

IBM Research used a weak supervision-based framework to develop a novel search, label, and propagate (SLP) architecture to bootstrap intent classification using existing chat logs.


more labels generated vs. hand-labeling


increase in model accuracy vs. hand-labeling


increase in precision vs. hand-labeling
Read case studies

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

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

With conventional approaches

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

Are you ready to dive in?

Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
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