- Helsinki startup CloEE raised a €520k pre-seed round (June 2025) to scale its AI-driven discrete manufacturing optimisation platform into the Nordics, Italy and the US.
- CloEE says its plug-and-play system can connect machines in about two weeks (on-prem, offline or cloud) and deliver gains such as +€867k annual revenue, ~30% lower energy use and 95% fewer emergency stops.
- The global AI-in-manufacturing market is forecast to surge from roughly $5–6B in 2024 to $130–230B by 2034, creating strong tailwinds for adoption.
- Key challenges are data quality and integration, change management and competition, making fast expansion, credible pilots and GenAI/agent differentiation critical.
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CloEE’s recent pre-seed raise of €520,000 positions it among the early movers in bringing AI-driven manufacturing optimisation tools to SMEs and mid-size manufacturers. With leadership from angel investors including Miro Vertanen, Innovestor Angel Co-fund, Cariplo Iniziative, and FiBAN members, the funding appears appropriately aligned for early expansion and R&D.
Its claimed performance metrics are compelling: connecting 25,000 machines across 25 countries, achieving up to €867,000 in annual incremental revenue, reducing energy usage by ~30%, and emergency stops by 95%. These results suggest a high ROI case, especially given CloEE’s assertion of deployment in as little as two weeks, and availability on-premise, offline, or cloud-based.
On the demand side, the macro environment is strongly supportive. Multiple reports place the global AI in manufacturing market at roughly USD 5-6 billion in 2024, with projected growth to between USD 130 billion and over USD 230 billion by 2034, and annual growth rates well above 35%. Key drivers include predictive maintenance, production planning, hardware/software transitions, generative AI, and sustainability pressures.
However, there are serious execution risks and open questions. Manufactures often struggle with data collection and integration; CloEE claims 70% of manufacturers fail to reach even 50% equipment efficiency due to inadequate data practices, highlighting a fundamental barrier. Implementation cost, internal talent, and change management remain concerns, especially among smaller manufacturers.
Strategic implications for investors and market entrants include:
- Focus on verticals with high downtime costs or energy intensity (e.g., automotive, electronics, heavy machinery).
- Leverage ease of deployment and flexible deployment options (on-premise, offline, cloud) to lower adoption friction.
- Differentiate through generative AI and agentic models; CloEE intends to explore AI Agents to potentially replace or augment traditional heavy-machinery platforms.
- Pursue strategic partnerships or pilots (for example with OEMs or large multi-facility customers) to build references and international credibility. CloEE’s ongoing pilot with a US customer operating 100 facilities, and discussions for a 3-year contract over 10 sites, suggest this path already underway.
Open questions include:
- How sustainable and generalisable are the claimed performance gains across diverse manufacturing settings?
- What margins can be achieved, considering deployment, support, and scaling costs?
- How will competitive pressures evolve, including from large incumbents and other AI startups?
- How will regulatory, data privacy, and cybersecurity considerations affect scalability, especially for cloud/offline deployments?
Supporting Notes
- CloEE’s pre-seed funding amount: €520,000; established in Helsinki; co-founders Oleksandr Zadorozhnyi (CEO) and Julia Sabitova (COO); target expansion to the Nordics, Italy, and US.
- Investors include angel investor Miro Vertanen, Innovestor Angel Co-fund, Cariplo Iniziative, and FiBAN members.
- Key impact metrics: up to €867,000 in annual revenue growth; 30% energy consumption reduction; 95% fewer emergency stops.
- Claim that 70% of manufacturers struggle to reach even 50% equipment efficiency due to inadequate data collection and analysis.
- Deployment capability: solutions can be deployed on-premise, offline, or in cloud in two weeks.
- Global AI in manufacturing market forecasts: e.g., USD 5.12 billion in 2024 → USD 132.54 billion by 2034; CAGR ~38.5%.
- Alternative forecasts: USD 5.94 billion in 2024 → ~USD 230.95 billion by 2034; CAGR ~44.2%.
- Segment growth trends: predictive maintenance, generative AI, machine learning, hardware/software component splits.