Thin integration layer around a managed AI API, where most intelligence lives in an external provider and the application focuses on prompts, inputs, routing, and post-processing.

Recommendation Systems (RecSys) predict what items a user is most likely to engage with, buy, or value, then rank and surface those items from a large catalog. They typically combine signals from user behavior, item attributes, and context using methods like collaborative filtering, content-based models, and deep learning–based ranking. Modern RecSys are end-to-end pipelines that ingest logs, build features and embeddings, train candidate generators and rankers, and continuously evaluate and update models in production.

Retrieval systems are AI and information-retrieval architectures that locate, filter, and rank relevant items from large data collections such as documents, web pages, logs, or databases. They transform both queries and content into searchable representations (keywords, embeddings, or structured fields), index them for fast lookup, and apply ranking algorithms to surface the most relevant results. Modern retrieval systems often blend lexical, semantic, and metadata signals, and they are foundational for semantic search, RAG (retrieval-augmented generation), and enterprise knowledge access.