Streamline AI search pipelines with SQL

Native filtering and reranking in modern vector databases lets you weave business rules (subscription tier, permissions, recency, customer lifetime value) directly into the search itself. The catch is that those attributes are usually denormalized from many operational systems, and keeping them fresh forces an unwelcome choice between stale results and expensive recomputation on every write or read.

Materialize collapses that tradeoff. Express the attribute logic as ordinary SQL views over your operational sources, index those views, and Materialize maintains them incrementally as upstream data changes. SUBSCRIBE to the deltas and push them into your vector database in batches. The work is proportional to what actually changed, not to the complexity of the transformation, so a single permission flip propagates to the index in hundreds of milliseconds, not minutes.

Most teams building production search ship the same architecture: a CDC stream out of Postgres, a transformation layer that turns raw rows into JSON documents with embeddings, and a writer that lands those documents in a search index. It works in proof of concept, then collapses under the weight of multi-tenancy, backfills, schema drift, and the operational burden of running stream processors in production.

Materialize replaces the middle of that pipeline with a SQL layer your existing team already knows how to operate. Sources connect over CDC or Kafka. SQL views express your document shape, filters, and tenant scoping. Indexes turn those views into incrementally maintained results. SUBSCRIBE streams precise deltas downstream, so your search index reflects writes within a second of them landing in Postgres, and your engineers stop owning a forest of services they didn't ask for.

Agentic systems put very different demands on a data layer than traditional applications. Writes arrive at machine scale (every enrichment, every inference, every human override) and the read path needs to flatten, reconcile, permission-check, and return context fast enough for an agent to chain reasoning steps without waiting. A 100ms query against a tuned OLAP database isn't enough; the data has to feel mutable, like the agent is touching a live representation of the business.

Materialize was built for this access pattern. Reconciliation logic (canonical fields, ranking, tenant scoping, RBAC) lives as SQL views over the full-fidelity write store. Those views are maintained incrementally and emit deltas as facts change, so the search index and any MCP server in front of it stay in lockstep with the source of truth. When a human edits a record or an agent makes an inference, every downstream context surface reflects the change in well under a second. Explainability comes for free: the views are the contract.