Why AI needs unified data infrastructure: Matia raises $21M series A

Today, I'm thrilled to announce that Matia has raised $21 million in Series A funding led by Red Dot Capital Partners, with participation from Leaders Fund, Secret Chord Ventures, Cerca Partners, Caffeinated Capital, and VelocityX, along with incredible angels including Karim Atiyeh (Ramp), Udi Mokady (CyberArk), Amiram Sachar (Upwind), Alex Pham (Toyota), Raffi Kesten, and Abe Peled.

This round brings our total funding to more than $31 million and marks an important milestone in our mission to build the AI-native data infrastructure that modern data teams need and build towards the vision of creating an AI data engineer. 

A lot has happened since we emerged from stealth fifteen months ago. We've grown more than 10x. We've earned the trust of data teams at companies like Ramp, Drata, Gloss Genius, HoneyBook, and Lemonade. 

And we've learned something critical: AI isn't just another feature to bolt on. It's a fundamental shift in how data infrastructure needs to be built, and we’re ready to tackle that head on.

‍

The AI paradox: everyone's building for it, few are building for it correctly

Every company is racing to build AI features. But here's the uncomfortable truth: you can't build reliable AI on unreliable data infrastructure.

I see this everywhere. Teams spend months training models, tuning prompts, and optimizing performance, only to realize their biggest bottleneck isn't the AI. It's the data layer underneath it. Quality issues that only surface after the model is in production.

The AI era demands more from data infrastructure, not less. It demands real-time reliability, end-to-end observability, and the kind of unified context that fragmented point solutions simply can't provide.

That's why we built Matia unified from day one.

Unified isn't a marketing term. It's a necessity

When we started building Matia, we made a deliberate choice: bring data ingestion, reverse ETL, observability, and catalog together in a single platform. I had lived the problem as disparate data tools as a Head of Data at Pangaea. My team was spending hours stitching together tools, and still not complete answers. 

The market is proving this thesis faster than we expected. Since January 2025 alone, we've watched more than a dozen point solution vendors consolidate through M&A. Companies that built their business on a single pillar are now scrambling to acquire their way into broader platforms and major data ecosystem creators are leaning in. Data warehouses like Snowflake are buying ingestion tools. Catalog companies merging with governance solutions.

Bolting together products built on different architectures, with different data models, and different assumptions doesn't give you a unified platform. It gives you a collection of tools with shared branding.

We built unified from day one, but it wasn’t just about reducing tool sprawl. Though our customers report up to 78% lower total cost of ownership compared to stitching together separate systems.

When your ingestion layer interacts with your observability layer, you can detect anomalies earlier in the data lifecycle. When your catalog understands lineage across the entire pipeline, impact analysis becomes automatic. When everything shares the same foundation, you get the kind of operational clarity that AI systems need to work reliably.

Ofir Ventura, Data & ML Manager at Lemonade, put it well:

"At our scale, data reliability matters as much as application reliability. Matia has helped our teams streamline how we move and operate data by providing a single platform we can run day to day as our data needs continue to grow."

‍

Where data engineering becomes AI-native

Being AI-native isn't about how you wrap AI into your product. It's about whether your system was architected to support AI at all.

With a unified foundation in place, we're now building something that couldn't exist on fragmented infrastructure: an AI data engineer that can autonomously handle pipeline creation, anomaly detection, impact analysis, and remediation. 

It's about giving teams of any size organization the ability to operate at the scale and reliability level they need. This way, a five-person startup can have the same data confidence as a five-hundred-person company.

Matia will become the Cursor for data engineering. The same way Cursor helps developers move 10x faster, Matia helps data teams build and operate better infrastructure without losing control. Matia will provide unified architecture, confidence and context to the rest of their data infrastructure.It’s a lofty vision, but I’m confident this team can make it a reality.

What comes next

Broader coverage across the data lifecycle. And continued investment in the people and the unified foundation that makes it all possible.

We're scaling our teams in both North America (GTM) and Israel (R&D). And we're doubling down on the vision that's driven us from the beginning: making world-class data operations accessible to teams of any size.

I also want to take a moment to say thank you to the incredible Matia team, our partners, our customers, and our investors.

Danielle Ardon Baratz, a Partner at Red Dot Capital Partners and someone in the venture community I’ve long admired, led our Series A and has been an exceptional partner from the start, bringing deep conviction, thoughtful advice, and a willingness to open doors through key introductions. We were also fortunate to have Gideon Hayden at Leaders Fund alongside us, the kind of partner every founder hopes for, experienced, deeply engaged, and consistently generous with his time, perspective, and support.

I’m also immensely grateful to all of our investors, including Omri Krigel from Secret Chord Ventures and Haleli Barath from Cerca Partners, who have been incredible partners throughout our growth.

And I’m especially proud that every single one of our seed investors chose to double down. That continued belief and conviction in Matia means more than we can put into words.

‍

I also want to thank my brother and co-founder, Geva Segal. You always hear sibling co-founders say when they were younger, they never could have imagined where they would be. Well, when we were younger, we knew exactly that we would be starting a company together. And it's even better than I could have imagined.

If you're a customer that’s building with Matia, thank you for trusting us with your data infrastructure. If you're considering it, I'd encourage you to reach out and see what unified, AI-native data infrastructure can do for your team.

If you're into the idea of building the future of data infrastructure, we're hiring. We are planning to double the team this year. 

This is just the beginning. The AI era demands data infrastructure designed for its requirements from the ground up. That's the foundation we've laid, and that's the future we're building at Matia. With that foundation, the AI data engineer can do its job.

Thanks for being part of our story. 

And if you think we’re going to take the rest of the week off, you don’t know Maita. Stay tuned for some more launches this week on Linkedin.