Snorkel AI positions itself as a data-centric enterprise AI company focused on accelerating production-ready models through programmatic data development, expert-curated datasets, bespoke LLM fine-tuning, and domain-specific evaluation/benchmarks. Core offerings include Snorkel Flow (an enterprise data development platform supporting programmatic labeling, annotation studio, prompt development, guided error analysis, model evaluation, and end-to-end ML app workflows) and Expert Data-as-a-Service (white-glove teams creating domain-specific datasets with multi-layer QA, rubrics, and traceability). Snorkel also provides Snorkel Custom for end-to-end co-development of domain-specific LLMs (data identification, knowledge capture, fine-tuning/alignment, evaluation, distillation, and deployment) and Snorkel Evaluate for custom LLM evaluations and benchmarks. The platform emphasizes evaluate → develop → deliver workflows, ingestion from diverse enterprise sources (databases, documents/PDFs, telemetry), integration with vector DBs and feature stores, and connections to model serving/observability stacks. Product and docs reference integrations with MLflow, AWS SageMaker, Google Vertex AI, and Databricks, and the documentation hub (docs.snorkel.ai) includes getting-started tutorials, API references, and release notes. Public information indicates enterprise-grade QA, traceability, evaluation/benchmarking (Terminal-Bench referenced), and recent release notes noting privacy/security features (2024.R3). Public site does not list pricing or trial terms; pricing appears to be enterprise/custom via sales engagement. Origin and research lineage point to work from the Stanford AI lab; leadership includes co-founder and CEO Alexander Ratner. Known customer/partner references on public pages include Wayfair, Anthropic endorsement, and large enterprise customers referenced in case studies.