Encord

Encord envisions a future where the creation and deployment of artificial intelligence are vastly accelerated through seamless integration of sophisticated tools and infrastructure. Our mission is to empower AI builders by transforming the complexity of data management into streamlined workflows that enable smarter, faster development aligned with the highest standards of AI precision and reliability.

At the heart of our platform is a commitment to solving the AI alignment challenge by equipping data teams with cutting-edge capabilities for handling diverse and intricate datasets, including multimodal and 3D sensor data. We harness the power of automation, advanced visualization, and real-time analytics to unlock new potential in AI applications across industries, advancing the frontier of intelligent systems with clarity and focus.

Driven by a foundation in quantitative science and innovative technology, Encord is building the infrastructure for the next era of AI innovation—where data-driven insights seamlessly translate into meaningful, impactful applications that redefine what's possible for enterprises and research organizations worldwide.

We've been watching Encord for a while, and honestly, they're tackling one of AI development's biggest headaches — data management. Founded by a team of former quants and physicists who clearly got tired of wrestling with messy datasets, Encord has built something that feels less like another AI tool and more like essential infrastructure.

What Made Us Take Notice

The numbers speak volumes here. When a company like OnsiteIQ sees 5x improvement in data throughput and cuts their time to value from two months to two weeks, that's not incremental progress — that's a game changer. We're particularly impressed by how Encord handles multimodal data, especially the complex stuff like 3D scenes and multi-sensor inputs from LiDAR and cameras.

Their SAM 2 integration for automated labeling caught our attention too. It's one thing to promise automation, but actually delivering workflows that reduce labeling costs while maintaining accuracy? That's where the rubber meets the road.

The Smart Infrastructure Play

What we find clever about Encord's approach is how they've positioned themselves as infrastructure rather than just another annotation tool. They're not trying to be everything to everyone — instead, they're laser-focused on the unglamorous but critical work of data curation and management that makes or breaks AI projects.

The fact that over 200 top AI teams trust them tells us they've figured out something fundamental about enterprise needs. Plus, backing from Y Combinator, CRV, and other top-tier investors with $30 million in funding suggests the smart money sees the same potential we do.

Who Should Pay Attention

If you're running an AI team that's drowning in data complexity — especially if you're dealing with physical AI applications or multimodal datasets — Encord deserves a serious look. We think they're particularly valuable for enterprises and research organizations that need to scale their AI operations without losing their sanity in the process.

The platform seems built for teams who've moved beyond proof-of-concept and need production-ready infrastructure. It's not for weekend warriors, but if you're serious about deploying AI at scale, Encord appears to remove a lot of the friction that typically slows teams down.