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Snorkel Enterprise AI Platform
Curate training data and deliver AI/ML models up to 100x faster with programmatic data development.
Accelerate enterprise AI development by transforming enterprise data into AI data, scaling subject matter expert (SME) domain knowledge and making it easy for AI teams to curate training data, evaluate GenAI systems, optimize RAG pipelines, and fine-tune LLMs.
The fast path to specialized AI for enterprises
GenAI evaluation
Create specialized evaluations for enterprise GenAI systems that apply custom acceptance criteria at scale.
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GenAI optimization
Improve retrieval with adaptive chunking and keyword extraction, and fine-tune or distill LLMs for improved responses.
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Predictive ML
Curate labeled training data with ease, and train custom models for information extraction and classification tasks.
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Trusted by world-leading data science teams
93%
Model accuracy
with just a few labeling functions
“We accurately labeled a few thousand pathology reports with one domain expert in days versus weeks using Snorkel Flow.”
Janet Mak
Deputy CIO and VP of Digital Solutions
85%
accuracy
on a classification model within days
“With Snorkel Flow, we cut labeling time and significantly accelerated model development when delivering NLP solutions.”
Catherine Aiken
CSET Director of Data Science and Research
What is AI data development?
AI data development is the process of transforming enterprise data and domain knowledge into AI data and applying it to create specialized AI/ML systems, whether AI assistants and copilots or complex predictive models. It replaces traditional manual processes with programmatic data development workflows that empower AI teams to accelerate and scale the delivery of specialized AI/ML systems.
The Snorkel Enterprise AI Platform
Snorkel's Enterprise AI Platform provides AI teams and SMEs with a collaborative platform for using domain knowledge and enterprise data to build specialized AI/ML systems via programmatic data development, evaluation/error analysis, and optimization.
GenAI evaluation
The Snorkel Enterprise AI Platform empowers AI teams to accelerate and scale the process of evaluating enterprise GenAI systems with specialized evaluators to apply SME acceptance criteria at scale, fine-grained metrics to derive actionable insights, and SME-in-the-loop workflows to ensure trustworthiness.
Learn more about GenAI evaluation
Specialized evaluators
Develop and test evaluators with an LLM-as-a-judge GUI to apply SME-defined acceptance criteria programmatically and at scale.
Fine-grained metrics
Categorize evaluation inputs based on business context or function via data slicing to identify failure types and severity with granular metrics.
SME-in-the-loop
Ensure evaluators reflect human judgement by soliciting ground truth and feedback, and monitoring SME-evaluator alignment metrics.
Corrective actions
Address identified failures by applying RAG optimization and LLM fine-tuning or distillation techniques from within the platform.
GenAI optimization
After evaluating GenAI systems against domain, business, and use case acceptance criteria, AI teams can improve response accuracy and quality through a combination of prompt engineering, RAG optimization, and LLM fine-tuning or distillation techniques via advanced AI data development methods and workflows.
Retrieval
Switch from token count to structural chunking, add keyword and metadata extraction, and fine-tune embedding models to improve retrieval accuracy.
Fine-tuning
Fine-tune open models on enterprise data so they perform domain-specific tasks with greater accuracy, reliability, and business alignment.
Distillation
Leverage foundation models by transferring advanced knowledge and reasoning capabilities into smaller, faster models with equivalent accuracy.
Predictive ML
Build advanced predictive models for enterprise classification and information extraction tasks, whether to classify the intent of AI assistant/copilot users or extract structured data from complex PDFs such as financial reports and research papers.
Curate training data up to 100x faster by encoding SME domain knowledge into label functions and applying them to an entire dataset at once.
Review model-guided error analysis results to improve label accuracy by discovering errors, conflicts, and low confidence levels.
Gather SME input and feedback efficiently by taking advantage of collaborative features such as ground truth annotation, tagging, and comments.
Train and deploy predictive models iteratively by refining the training data, algorithm, or parameters, and deploying them as MLflow packages.
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