Edge AI is a production requirement across automotive, robotics, and industrial verticals, but the infrastructure underneath it doesn't exist. Every team deploying models on edge devices rebuilds memory management, platform abstraction, and monitoring logic from scratch. The hardware is fragmenting across dozens of chipsets and runtimes. No single vendor's tools cover the stack. The developer experience is years behind what cloud teams take for granted.

The opportunity: Build the software and tooling layer that makes edge hardware usable — frameworks that abstract across platforms, memory managers that optimize dynamically, observability stacks that surface what's happening inside a deployed model running on a sensor or a vehicle. Start with the highest-cost-of-failure verticals (autonomous vehicles, industrial robotics), then expand into the full edge AI developer stack.

A few examples:

Edge Observability and Developer Tools

Teams deploying models at the edge have no visibility into what those models are doing in the field. Inference latency, memory pressure, thermal headroom: none of it is legible. When a model degrades, debugging is manual and slow.

Memory Management and Testing for Edge Deployments

Edge devices operate in conditions data center memory was never designed for: temperature extremes, vibration, extended duty cycles. Memory allocation across a fleet is static and manual. Performance degradation goes undetected until failure.

Hardware-Agnostic Frameworks

A model that runs on one edge chipset requires substantial rework to run on another. There is no universal SDK, no abstraction layer that makes inference logic portable. Vendor lock-in is the default, not a choice.

Memory Architectures and Processing-in-Memory

For vision processing, sensor fusion, and real-time control, the cost of moving data between memory and compute is measured in power, latency, and heat. Data center DRAM was not designed for these tradeoffs. Application-specific memory systems for edge workloads don't yet exist at scale.

We're looking for Founders to leverage their domain expertise, and care deeply about the developer experience.

You've shipped inference at the edge and hit the wall on memory, latency, or platform lock-in. You've debugged a model that ran fine in the cloud and failed on the device. You know the gap isn't the model. It's the infrastructure underneath it.
are convinced AI can deliver the outcomes better than humans to build full-stack, AI Native services in industries where outcomes depend on expertise, regulation, and trust.