MODEX 2026 in Atlanta delivered - incredible conversations, sharp insights, and a ton of momentum across the logistics space. We spent time connecting with teams navigating a common challenge: disconnected ERPs, warehouse systems, and transportation systems that leave operators flying blind. There’s a clear shift happening toward turning siloed data into reliable, up-to-date data products - so operators, applications, and AI agents can act on fresh information in seconds, not hours. If we didn’t get a chance to connect, would love to continue the conversation: https://lnkd.in/e4dUFJiH Looking forward to staying in touch and building what’s next. #MODEX2026 #MODEX Conferra.ai
Materialize
Software Development
New York, NY 8,261 followers
Transform siloed data into up-to-the-second context, just using SQL
About us
Materialize is the context engine for AI agents and applications. It lets engineering teams use SQL to transform siloed operational data into trustworthy, real-time data products. Use Materialize to deliver fresh context for AI agents, power data-intensive UIs, and create low-latency, event-driven architectures that support microservices and core business processes. Materialize is trusted by General Mills, Bilt Rewards, and Crane Worldwide Logistics to solve their most pressing operational data challenges while building a live data foundation for their AI transformation.
- Website
-
https://materialize.com
External link for Materialize
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- New York, NY
- Type
- Privately Held
Locations
-
Primary
Get directions
New York, NY 10013, US
Employees at Materialize
Updates
-
Our April 2026 newsletter is out. This month, we’re focusing on a pattern we’re seeing across industries: teams moving from fragmented, batch-based systems to real-live views of their operations. Whether it’s logistics, finance, manufacturing or beyond, the challenges are similar - data scattered across systems, pipelines that introduce latency, and engineering time spent stitching things together instead of building product. What’s changing is the shift to live data. At Materialize, we’re working with teams that are: → Moving from stale snapshots to live visibility → Reducing latency from minutes to milliseconds → Building data products that compound into multiple use cases The result: a dynamic “digital twin” of operations—powering applications and AI systems with up-to-date data. We also cover product updates, performance improvements, and upcoming events.
-
Stop serving your agents and microservices stale data. Build a Chipotle kitchen for your data products.
It’s incredibly hard to serve AI context that’s both fast and fresh. You either get an operational database that blows up under complex queries, or a data warehouse with fast queries that's fundamentally stale. Chipotle figured out the same problem in a different domain. They don't wait for you to order before starting work. They continuously transform raw ingredients into semi-finished goods, grilled chicken, guacamole, so when you show up, assembly takes seconds. The reactive part is fast because most of the work is already done. The same idea applies to your data infrastructure. Stop waiting for agents and clients to request data before you start doing the work. Continuously transform your raw operational data into real-time data products. What is a real-time data product? Unlike a one-off query, a data product is designed to be discoverable, reusable, and composable across teams and services. For apps and agents, it also needs to serve thousands of concurrent reads at millisecond latency, and reflect changes in the source system within seconds. When you build this way, agents can take an action, see the results, and decide what to do next, all within a tight enough loop to support real human-agent collaboration. Without it, you're giving agents (or traditional microservices) a worldview that's drifting further from reality every second. We built Materialize to do exactly this, and we're seeing it deployed for real-time data products across enterprises and startups alike.
-
-
We’re heading to MODEX 2026 in Atlanta next week. Materialize will be onsite connecting with teams building real-time, operational supply chains - from live visibility to AI-driven systems powered by up-to-the-second data. If you’ll be there, we’d love to connect. Whether it’s a quick coffee, demo, or dinner, let us know what works best for you. Let us know you’re attending: https://lnkd.in/ehM3xDxK #MODEX26
-
-
Join us today at 3 PM ET for “Real-World Context Engineering – AI Agents in the Enterprise” and learn how teams are building AI agents with live, reliable data. It’s not too late to register and you’ll receive the recording if you can’t attend live. Speaker: Nate Stewart, CEO at Materialize Webinar Registration: https://lnkd.in/eu95dBzK
-
AI agents don’t fail because of the model - they fail because of the context. In our latest blog, we dive into enterprise context engineering and why live, continuously updated data is becoming the foundation for reliable AI systems. If you’re thinking about deploying AI agents (or already have), this is a shift worth understanding. Read more: https://lnkd.in/e8rVm5_r
-
-
Great event at Data Streaming World Tour yesterday. Thanks to everyone who joined our session, “Real-world context engineering: AI Agents in Production.” We had some great conversations around what it actually takes to move AI agents from experimentation into production - especially the role of live data, context, and reliability. If we didn’t get a chance to connect, would love to continue the conversation: https://bit.ly/4l52FGN We look forward to the next Data Streaming event in Chicago on May 14th. #DataStreamingWorldTour Confluent
-
-
AI agents are moving from experimentation to production - but without the right context, they break. Join us next week for our webinar: “Real-World Context Engineering – AI Agents in the Enterprise” 📅 Tuesday, March 31 ⏰ 3:00 PM ET We’ll cover how leading teams are powering AI systems with live, continuously updated data — and what it takes to build reliable, scalable agents in production. You’ll learn: - Why live context is critical for AI reliability - How to move beyond siloed data - Proven architectural patterns for live AI systems - Best practices for maintaining freshness and trust Speaker: Nate Stewart, CEO at Materialize Register here and to get the recording: https://lnkd.in/eu95dBzK
-
-
Great insights from Seth Wiesman here. We’re seeing this firsthand - as AI systems move into production, live, continuously updated context is what enables agents to take reliable action. That shift is driving a new wave of architecture decisions across the enterprise. Watch the full episode on #MillenniumLive: https://lnkd.in/eMipeh6c
AI isn’t just generating anymore — it’s acting. In our latest #MillenniumLive episode, Seth Wiesman, Field CTO at Materialize, breaks down why agent-driven systems are reshaping enterprise architecture, what “time to trust in action” really means, and why real-time data is becoming a competitive edge. Tune in here: https://lnkd.in/eMipeh6c
-
What actually makes live systems fast? In our latest blog, we take a deep dive into how we’re speeding up Timely Dataflow - the core engine behind Materialize and what it means for building low-latency, scalable data systems. Timely Dataflow powers streaming computation by tracking progress and updating results incrementally as data changes, enabling live results without recomputing everything from scratch. If you’re building systems that rely on fresh, continuously updated data, this one’s worth a read. 👉 https://lnkd.in/eUd7V7eh
-
