Streamline AI search pipelines with SQL
Keep vector embeddings and search index attributes continuously up-to-date with real-time, integrated data products built from your operational data.
A flexible foundation for AI-native search
Vector databases and search indexes are only as good as the context you feed them. Modern AI systems need context that is flattened, ranked, permission-aware, and fresh enough that an agent’s view of the world stays in sync with reality.
Materialize is a different building block. It ingests operational data continuously over CDC or Kafka, lets you describe the canonical entities of your business (a contract, a case, an order) and the context your indexes need in SQL, and keeps them up to date incrementally, doing the minimum work required to reflect every upstream change. The result is one declarative pipeline that streams precise deltas into Elastic, OpenSearch, Turbopuffer, or any other index, with end-to-end freshness measured in hundreds of milliseconds, plus a single surface to recreate the full record post retrieval.
Why is it so hard to keep your search index fresh?
OLTP Databases
Your OLTP databases are siloed and assembling a flat, denormalized search document means heavy joins twice: once to create the document you put into the search index, and again on read to rehydrate the results.
Data Warehouses
Warehouses can do the heavy integration and denormalization work, but they run in periodic batches, often measured in minutes to hours, behind reality, drifting further from the truth after every batch. At that latency you can’t have an interactive agent that teams with humans, one that writes to upstream systems and sees the result of that write in its context moments later.
Do-it-yourself
Building custom pipelines using stream processors like Flink mean complexity, specialized talent, and low agility. Adding a new aggregation or join can be weeks of effort to get into production.
Sync your search index in real-time
As more agents are being powered by search indexes, the complexity of keeping them up to date is stalling projects, exploding budgets, and leading to stability issues. When it does work, you often can't change anything.
Materialize replaces all of that complexity with a simple SQL views you can subscribe to. Sources connect over CDC or Kafka. Define your document shape in SQL and Materialize keeps it up to date, pushing precise deltas to your search index within a second of a any change to an upstream source.
"AI has put massive amounts of raw truth in play that we couldn't work with before. Materialize gives us a flexible platform for turning that into live context, in a way that matches how an agent would want to read it."
Vector search and re-ranking on trustworthy data
Modern vector databases let you weave business rules like subscription tier, permissions, and other attributes directly into an agent's semantic search. The catch is that those attributes are often denormalized from many operational systems, and keeping them fresh forces a choice between stale results and expensive recomputation on every write.
In Materialize a single upstream write can update the exact attributes and embeddings required within hundreds of milliseconds, even if the business rules to determine what changed are extremely complex. The same features power your re-ranker, so relevance reflects the latest state of your business.
Learn how to build vector pipelines that stay fresh at scale — without constant reembedding.
Up-to-the-second reconciled context
To avoid excessive search index writes, most teams store a partial entity in the search index. On read, the result is rehydrated by joining and transforming data from multiple source databases and microservices before an agent can act. The work to create the full canonical representation of a business object (customer, an order, a portfolio) can require many hops, require high join cost, and potentially destabilize operational systems.
Materialize collapses those enrichment steps into a single low-latency lookup, because the context that the agent needs to join with is incrementally kept up to date as the world changes, so the search index and any MCP server in front of it stays in lockstep with your systems of record. When a human or agent makes an insert, update, or delete, every downstream data product reflects that change at extremely low latency, and agents can get the full canonical record back in a single read.
Three primitives, end to end
Three primitives turn ordinary SQL into a continuously fresh pipeline from your operational data to any vector or search index.
Connect Postgres, MySQL, SQL Server, Kafka, and other operational systems.
Express your document shape, filters, scoring, as SQL-defined views. Materialize maintains it continually as upstream data changes.
Subscribe to updates and sync them to your search index directly, through Kafka, or using Iceberg.
Built for AI-native search
Stream in from databases over CDC, Kafka, and webhooks. Publish to vector databases, search indexes, and downstream systems.
Joins, aggregations, windows, CTEs. Everything you'd expect from Postgres, with semantics preserved across incremental maintenance.
Index a view to make it incrementally and continuously maintained. Point lookups respond in ~10ms.
SUBSCRIBE for precise deltas as upstream data changes. No polling, no diffing, no drift.
Transactional boundaries preserved across views. One upstream change produces exactly one downstream event.
Express hierarchical, fine-grained access control and tenant scoping using standard SQL.