This is about teaching factories to "take care of themselves." Machines learn to warn you before they break, adjust their own settings for quality and efficiency, and eventually coordinate with each other so the whole plant runs with less human babysitting and fewer surprises.
Reduces unplanned downtime and maintenance costs, improves production efficiency and quality, and addresses skilled labor shortages by automating monitoring, diagnostics, and some decision-making inside plants.
Proprietary operational data (machine telemetry, maintenance history, quality outcomes) and tight integration into specific plant equipment and workflows create switching costs and continuous performance improvement over time.
Early Majority
Positioned around the full journey from basic predictive maintenance toward higher levels of autonomy at the plant level, implying integrated use of time-series analytics, ML, and possibly vision across equipment rather than point solutions on single machines.
Think of Pelico as an air-traffic control tower for a factory’s supply chain. It continuously watches orders, inventory, suppliers, and production, then tells planners and buyers where problems will appear and what to do about them before things go wrong.
Imagine every product on your factory line being inspected by millions of tireless, super‑focused digital eyes that never get bored and learn from every defect they see. That’s what AI‑powered quality control does: it watches, learns, and flags issues in real time so bad parts don’t leave the factory.
This is about using AI as an always-on control tower for the factory-to-customer chain: it watches demand, suppliers, production and logistics in real time, spots problems early, and suggests better plans so you can change course quickly without chaos.