The robotics industry is undergoing a profound shift. After decades of slow, hardware-driven progress, we are finally entering an era where learning-based systems, generalist models, and large-scale data pipelines are beginning to transform how machines perceive, act, and adapt in the physical world. Yet while model capabilities have accelerated dramatically, the underlying infrastructure that robotics teams rely on has not kept pace. Engineers are still blocked by brittle tooling, fragmented systems, and data pipelines that make iteration painfully slow.

This gap between what AI can do and what robotics infrastructure enables is widening quickly. It is also one of the biggest opportunities of the decade. That is why we are thrilled to lead the pre-seed round in Neuracore, and to partner with the team on their journey while building the cloud-native data foundation that modern robotics has long needed.

A broken foundation: the data bottleneck slowing robotics

Despite extraordinary progress in model capabilities, robotics teams still operate on infrastructure designed for another era. Data is the core input to robot learning, yet the way it is captured, synchronized, stored, and reused remains deeply constrained. Unlike software-only AI domains, robotics generates multi-modal, high-frequency data streams. In today’s status quo, teams typically log these data streams through ROS (Robot Operating System), which requires synchronizing all sensor modalities to the lowest common denominator, leading to irreversible data loss and inflexible pipelines that break as soon as a team changes a sensor, embodiment, or task. Teams routinely rebuild logging systems from scratch, often multiple times, diverting scarce engineering talent away from innovation and toward maintenance.

Why now: the Physical AI inflection point

The timing for a new infrastructure layer could not be better. The shift from scripted robotics to learning-based systems is accelerating, and the demands on data infrastructure are expanding just as quickly. Generalist robot models require orders of magnitude more data than previous systems, often across thousands of hours, multiple sensor modalities, and varied embodiments. At the same time, advancements in multimodal foundation models are enabling robots to learn more efficiently, transfer knowledge across tasks, and operate with significantly reduced human supervision.

These technical shifts coincide with powerful market tailwinds. The global robotics market is projected to exceed $260 billion by 2030, growing at roughly 20% annually. Within that, robotics companies need to shift from handcrafted systems to scalable, data-driven architectures. The rise of simulation platforms, more accessible compute, and the rapid expansion of industrial and service robotics are further accelerating demand. Physical AI is no longer experimental; it is becoming the default paradigm, and it depends entirely on high-quality, flexible, large-scale data infrastructure.

Enter Neuracore: the cloud-native infrastructure layer for robotics

Neuracore addresses the single most cited pain point for robotics founders and engineers: data infrastructure. Hiring talent to rebuild logging pipelines is a major bottleneck for robotics companies and end users. By abstracting away the complexity, Neuracore can become the default foundation for robotics data, similar to how Databricks became the standard in ML data infrastructure.

At its core, Neuracore is building an end-to-end platform that allows robotics teams to capture every sensor at its native rate, log asynchronously, and synchronize only when needed – whether for visualization, training, or post-processing. This simple but powerful abstraction unlocks higher-fidelity datasets, radically faster iteration, and a far more flexible development workflow. And this is just the starting point. By owning the data layer, Neuracore is positioned to become the natural home for training, deployment, and the broader suite of tools robotics companies depend on.

If you want a deeper context on Neuracore and the robotics data stack, here are a few media pieces:
Tech Funding News, Tech EU, The Top Voices, Pathfounders, EU-Startups.