Why Robots Don’t Fail Because of Hardware

Hardware excellence doesn’t guarantee deployment success. Robots fail at scale because models haven’t seen the world they’re deployed in.

Healthcare, defense, logistics—across domains where mistakes cost lives and dollars, edge cases define success. Yet most training pipelines are built on narrow, sanitized datasets that don’t reflect real operational variance.

Here’s the macro context investors should care about:

* Service robotics alone is expected to grow from ~$24B in 2022 to ~$105B by 2032 (≈16% CAGR) as hospitals, labs, and enterprise environments adopt intelligent systems.

* Meanwhile, robotics-as-a-service (RaaS) is forecast to expand from ~$12.9B in 2024 to ~$157B by 2035 (≈25% CAGR), underscoring demand for deployed, maintained robotics solutions.

* And the foundational data annotation market—the layer underpinning effective machine vision and autonomy—is scaling rapidly, with some forecasts predicting growth into the tens of billions over the next decade.

Growth in robotics without corresponding investment in context-rich, labeled data is like designing airplanes without wind-tunnel testing. It puts deployment timelines and safety margins at risk.

That’s the core insight driving our next phase: specialty data products designed for robotics systems operating in real environments = structured capture, annotation, quality assurance and validation pipelines tailored to mission demands.

This is where predictive models move from academic benchmarks to operational reliability—and where data infrastructure becomes a strategic moat.

Let’s talk about how we think about value creation at the intersection of data and autonomous systems.