AI workflows manage the orchestration of data pipelines, model training, validation, and deployment across machine learning systems. They coordinate tasks such as data ingestion, preprocessing, training runs, evaluation, and model promotion into production. By structuring these processes, teams can build, test, and deploy AI systems more reliably, efficiently, and at scale.
17 workflows
End-to-end RAG knowledge base ingestion pipeline covering document intake, text extraction and chunking, embedding generation, vector store indexing, retrieval quality validation, catalog update, and stakeholder notification.
Escalation workflow for knowledge base refresh failures or stale content alerts. Holds for review, runs parallel re-extraction and re-indexing, then routes to resolution or manual intervention.
Fast-path indexing for short text snippets such as support articles, product descriptions, or FAQ entries. Skips OCR extraction and goes directly to embedding generation.
Batch ingestion of multiple documents concurrently using a Map state. Supports scheduled full-corpus re-ingestion and large document library imports.
Batch retraining of multiple models concurrently using a Map state. Supports scheduled monthly retraining runs across all production models.