Snorkel AI

Snorkel AI is a Data & Analytics tool that specializes in expert data development for frontier AI models and agents. It helps organizations build specialized training data, benchmarks, and evaluation environments to improve model performance in high-stakes domains.

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Pricing

Likely Not Free · Enterprise · Custom

Free tier

API

Skill level

Technical

About

What is Snorkel AI?

Snorkel AI provides a platform for expert data development, enabling frontier AI labs and teams to create specialized training data and environments. It addresses the challenges of generic data pipelines by focusing on distributional gaps, benchmark blind spots, and tasks where correctness is hard to define. The platform offers Snorkel Data Series for curriculum-structured datasets and custom data development for bespoke needs. Additionally, Snorkel AI helps build specialized agents grounded in expert data, evaluated in real workflows with programmatic pass/fail criteria. The methodology emphasizes calibrated expert review, rubrics, programmatic checks, and adjudication to ensure high-quality data that drives model improvement.

Best used for

Ideal for developers and data scientists who need to develop specialized training data, create custom evaluation environments, and build high-performing AI agents. Especially valuable for organizations working with frontier AI models that require precise data and rigorous testing in high-stakes domains.

Common actions

develop training data

evaluate AI models

build specialized agents

improve model performance

"AI Agents"

Capabilities

Key features

Expert-curated datasets
Custom data development
Specialized agent building
Evaluation environments
Programmatic checks
Research-led development

Target Audience

developerdata scientistproduct managerstartup founder

Integrations

Not yet documented

Pricing & Plans

Likely Not Free · Enterprise · Custom

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FAQs

What is a labeling function in Snorkel AI?

A Labeling Function (LF) is an arbitrary function that takes a data point and outputs a proposed label or abstains. It captures subject matter expertise in code, integrating existing resources or user-provided heuristics to rapidly build new training sets and machine learning models.

How does Snorkel AI handle conflicting labels from labeling functions?

Snorkel AI uses a label model to automatically learn how to optimally combine noisy and conflicting labels from various labeling functions. This approach resolves conflicts without requiring explicit enumeration of every potential rule conflict, often without needing ground truth labels.

How is writing labeling functions different from feature engineering?

Labeling functions are used for creating labeled training sets, independent of the model or features used. While they may reference existing features, their primary role is to specify a feature and its associated class, helping to generalize the final ML model rather than relying solely on referenced features.

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Business Intelligence Predictive Analytics Data Labeling & Annotation Real-Time Analytics Market Research Data Cleaning & Prep

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