Snorkel AI Data Development Platform

Snorkel Develop

Tune and optimize specialized AI systems with the power of your expertise and data — including LLM fine-tuning, reinforcement learning, RAG optimization, and more

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Enterprise GenAI requires specialized systems

It's easy to build prototypes of enterprise AI assistants and copilots with off-the-shelf components, but they inevitably lack the accuracy and reliability needed for production deployment. The solution is to optimize components of your agentic AI systems, including fine-tuning LLMs and RAG pipelines, using enterprise data and domain knowledge to create specialized GenAI systems which are adapted to specific domains and use cases.

GenAI optimization with the Snorkel AI Data Development Platform

Curate high-quality LLM training data faster

Create diverse datasets of prompt-response pairs orders of magnitude faster by incorporating enterprise data and SME domain knowledge with the latest programmatic data development and synthetic data generation techniques, removing the need for manual efforts that can take weeks or months.

Gather SME input and feedback with ease

Collaborate with SMEs using a single platform to create ground truth based on domain knowledge and human feedback, and to iterate on training data by refining its quality and diversity to further improve and align LLM generation with business expectations.

Fine-tune and deploy specialized LLMs

After curating high-quality training data, use it to fine-tune and deploy specialized LLMs by taking advantage of native integrations with AWS SageMaker, Google Vertex AI, Azure Machine Learning, Anthropic, OpenAI, HuggingFace, Databricks Mosaic AI, and more.

Fine-tune embeddings for domain accuracy

Improve RAG pipelines efficiently by curating training data with programmatic data labeling and synthetic data generation, and using it to fine-tune embedding models from leading LLM makers—significantly improving retrieval accuracy without having to modify source documents or code.

Add document metadata to improve retrieval

Apply programmatic information extraction to label document chunks with helpful metadata before indexing them in a vector database, enabling AI teams to improve search accuracy and latency by retrieving relevant chunks by extending similarity search with filtering.