We build the operating system that trains, deploys, and scales humanoid robots in physical retail in the EU. Hardware is converging. The software layer is what wins.
Shelf replenishment and night-shift logistics are impossible to staff across the EU. According to the European Commission, more than two-thirds of mid-sized EU companies cite labour and skills shortages as a major obstacle to investment. The demographic pressure is structural — it will not reverse.
Industry average out-of-stock rate is 8%. Physical inventory accuracy sits at just 63–65% while the product is physically in the back room. Lost sales, frustrated customers, manual root-cause analysis.
89 million tonnes of food are wasted annually in the EU. A typical supermarket discards ~€2,300 in expired products every single day. Poor rotation logic, inconsistent shelf checks, no real-time visibility.
For the first time, three forces meet simultaneously: humanoid hardware reaching commercial readiness, the deepest labour shortage in decades, and AI/computer vision mature enough for real-time retail operations. The window is now.
Like Android operating different manufacturers' phones, Roboshelf AI runs any humanoid robot in any retail store — training it, deploying it, and managing its entire operation.
We don't bet on one robot manufacturer. Roboshelf AI works with any humanoid hardware — the platform is the moat, not the metal. When the hardware market consolidates, we win either way.
Every store deployment generates proprietary training data — product images, shelf scenarios, robot movements. This dataset is the accumulative advantage that becomes harder to replicate over time.
AI infrastructure built on European soil sits inside the EU's tech sovereignty priority — a structural tailwind across innovation funding, retail partnerships, and policy alignment. GDPR compliance is native to our stack, not retrofitted. We operate in the same market as our customers.
UnifoLM-VLA-0 fine-tuned on 1,000 human demonstrations of a shelf push task. Evaluated over 50 independent MuJoCo simulation episodes. Achieved 80% task success rate — 10× improvement over the v1 baseline in a single iteration cycle, at $0 infrastructure cost.
The pre-seed round is aimed at hiring a robotics engineer and AI training expert to build Milestone 3.