Neuracore has raised £2.2m in pre-seed funding to simplify the infrastructure that robotics teams use to collect data, train models and deploy AI systems. The round was led by Earlybird Venture Capital with participation from Clem Delangue and other unnamed industry experts.
Its cloud-native platform replaces fragmented, manually assembled pipelines and aims to reduce the time from robot data to deployed machine-learning models from months to days. The company supports developers in research labs and commercial robotics teams across healthcare, industrial automation, agriculture, warehousing and service robotics.
Neuracore provides a unified stack intended to replace disconnected, home-grown setups that commonly mix multiple tools. The platform supports imitation learning, reinforcement learning and transfer learning, while accommodating custom robot configurations. The company reports more than 50 organisations using the platform, spanning commercial robotics teams and academic research groups, and says it has partnerships with major hardware manufacturers to streamline real-world testing and deployment.
Robotics teams often spend significant engineering time constructing bespoke data pipelines before they can test learning algorithms. Neuracore addresses this bottleneck by offering an integrated cloud environment for data ingestion, storage, labelling, experimentation and model deployment. If successful, this could lower the barrier to deploying robot learning in production and improve iteration speed across sectors.
The raise comes as robotics shifts toward data-centric approaches, with organisations seeking platforms that handle large-scale data collection and model lifecycle management. For UK and European developers, cloud-native infrastructure can reduce duplicated effort and free teams to focus on application-level challenges. Neuracore positions itself against older robotics infrastructure providers such as Clearpath Robotics and Covariant, stating that its cloud-native architecture offers deeper integration for data-first robot learning workflows.
The company plans to use the new funds to expand its engineering team, accelerate product development and grow its open-source and commercial user communities. It also intends to scale a free academic programme to support research groups globally.
