Power AI apps and agents with fresh business context

As AI initiatives move from pilot to production, success depends on fast, reliable access to fresh business context. But with overloaded operational databases and hours-old warehouses, teams are forced to choose between slow applications or acting on stale data. Materialize is the foundation for low-latency context engineering, transforming operational data into live data products that are always fresh and fast to query. Power AI agents, semantic search, and ML features with up-to-the-second context, all using SQL.

AI agents and LLMs need fresh data to be effective. But operational databases are slow and struggle under query load, forcing agents to waste tokens joining and shaping data instead of making decisions. With Materialize, you can give agents fresh business context that is precomputed and kept up-to-date as source data changes. Define business entities like Customer, Order, or Inventory as live data products, let agents discover them through MCP, and serve query results in milliseconds. With fast access to live data, agents can quickly see the results of their actions and make better decisions.

Semantic search needs fresh vector embeddings and attributes for accurate results. But when vectors are derived from multiple data sources, it's hard to track what has changed and when, meaning reembedding either too often or not enough. Materialize enables you to model vectors as live SQL views that are incrementally updated when source data changes. Join across data sources, transform into documents and attributes, and stream updates over Postgres or Kafka. You have complete control over when to reembed, avoiding unnecessary costs while keeping search results fresh.

ML models need access to fresh data to make accurate predictions. But calculating data transformations like spend-in-last-5-minutes or transaction-count-by-merchant on live data is hard, especially with multiple data sources. With Materialize, you can define live features in SQL that stay fresh and up to date as source data changes. Join data from multiple sources, apply aggregations, window functions, and recursive queries, and serve features in under a second using a standard Postgres connection.

Learn more about Materialize, architectural patterns, and use cases.