The live context layer for agents and apps
Use SQL to transform siloed data into interactive context you can trust.
Trusted by engineering leaders
Why is it so hard to keep up with demands for fresh context?
OLTP Databases
Siloed and can't handle data-intensive queries.
Data Lakehouse
Lacks the freshness and cost-efficiency required for online services.
Do-it-yourself
Complex to operate, often requiring specialized talent.
Introducing Materialize
Live context you can trust.
Backed by the best to bring a breakthrough in incremental computation to the enterprise.
Integrate data from any source
Continually and incrementally ingest data from your databases, ERPs, CRMs, and other systems, unify it, and clean it for downstream consumption. Materialize grows more powerful as you connect more sources. Use our secure and scalable SaaS or keep everything air gapped on prem. Materialize separates storage from compute, scaling beyond local memory giving you economical processing for your most demanding operational workloads.
Use SQL to create real-time data products
Transform raw updates into live, canonical business objects that can be queried directly or pushed to downstream systems in real-time. Use Materialize to build a trustworthy, up-to-the-second digital twin of your business that streamlines agent and microservice development. Our groundbreaking incremental computation engine performs the minimal work to keep things up to date as updates happen, without destabilizing your operational databases. Any engineer who knows SQL can now launch a new data product or contextual building block for AI in minutes.
Rapidly deploy use cases from a live context graph
Link your real-time data products into a continuously updated context graph that agents and services can query directly. Offload complex queries from your MCP endpoints and APIs onto incrementally computed data products with single-digit millisecond latency. Keep vector embeddings fresh as source data changes, enabling RAG that's so fresh it's interactive. Every application and agent stays grounded in a trustworthy, up-to-the-second view of your business.
Tight agentic feedback loops
Write to your operational database and see the result in Materialize faster than you could assemble it by querying that database directly. Materialize keeps context incrementally up to date as data changes, so agents see the effect of every tool call in about a second, not after the next query or batch run.
In 3 minutes, an agent closes about 17 loops on OLTP, 2 on OLAP, and 178 on Materialize. One loop takes about 10 s on OLTP, 70 s on OLAP, and 1 s on Materialize.
Trusted by engineering and data teams
Customer Story: Bilt
Bilt uses Materialize as a live context layer for its AI concierge, search, and personalization experiences. By turning operational data from across the company into fresh, canonical entities, Materialize reduces the number of tool calls and integrations Bilt’s AI systems need, helps them use less expensive models, and keeps search systems continuously updated with relevant, up-to-the-second context.
Support your most demanding AI initiatives with your existing team
Your operational databases are great at processing transactions, but they weren’t designed to continuously assemble the complex context agents and APIs need. Materialize offloads that work by continuously transforming operational data and serving the results directly to agents and applications—reducing load on your source databases, improving stability, and giving you better scaling characteristics.
Vector indexes need to stay fresh as operational data changes, but keeping documents and embeddings synchronized across many upstream sources is difficult and expensive. Materialize uses SQL to continuously maintain the exact shape your index needs, incrementally recomputing only what changed and pushing those updates to Elastic, OpenSearch, Turbopuffer, or any other index. The result is fresher search with less custom pipeline code and far less unnecessary recomputation.
Microservices make systems easier to scale independently, but they make it harder to read across the business: data gets split across services, databases, and APIs, and every new feature can require coordination with another team. Materialize continuously joins that operational data into live, canonical business objects using SQL, so developers can build against a shared context layer instead of stitching data together service by service. The result is less cross-team coordination, fewer custom APIs, and much faster iteration on new features.
"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."
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Erik MunsonFounding Engineer, Day AI
"Our fraud losses have substantially decreased, and the infrastructure spend for this system has gone down by about 80 percent."
Read Customer Story
Brandon RochonHead of Data, Neo Financial
"Loan eligibility rule calculation time decreased by 98.15%, dropping from 27 seconds to half a second."
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Wolf RendallDirector of Data Products, Vontive
"We serve most concierge traffic on fast, efficient models and get frontier-quality answers — because the context does the heavy lifting, not the model."
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James DoradoVP of Data Analytics, bilt
"We had been debating one new fraud signal for three months and getting nowhere. Now I go to the data directly, build the feature, and test it the same day."
Anson YuDirector of Engineering, zepz
Integrate Materialize into your existing stack
Start by offloading live context assembly
To get a quick win, add Materialize alongside Databricks, Snowflake, or ClickHouse. Rather than rebuilding context from scratch on every batch run, Materialize keeps it current as the data changes. Tight agentic loops become possible: agents and services see the effect of a tool call or API write right away, not on the next refresh.
Then create a canonical context model
Over time, use Materialize to hold the up-to-the-second view of your business that every system and human agrees on. Agents query it directly via MCP, and the same transformed context flows downstream to power search or feed data lakes and warehouses for historical analysis.
