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Zurich's AVIAN closes $2.6M pre-seed to scale industrial fire prevention across high-risk sectors



May 21, 2026 - 2 min read

Both industrial fires and equipment failure share the same early warning sign: heat. In sectors such as wood processing, recycling and biomass, where materials are processed, the critical assets most likely to cause a shutdown - bearings, conveyors, motors and electrical cabinets - give off measurable thermal signals hours before they fail. However, most facilities still rely on periodic manual inspections and handheld thermography cameras that are checked at most once a quarter. Between inspections, equipment can go from a normal operating temperature to the ignition point without anyone noticing.

AVIAN, a Zurich-based industrial AI company co-founded by Drew Hanover and Thomas Laengle, has built a platform that turns continuous thermal monitoring into an operational response layer. Thermal cameras monitor critical assets 24/7, and AI models trained per asset learn what normal operating heat looks like for each individual piece of equipment. When the system detects an early drift from that baseline, it triggers a filtered alert (routed via phone call, SMS, WhatsApp, email, or PLC integration) before the anomaly develops into a failure or fire. Every alarm event is reviewed by the team and fed back into the models, improving detection accuracy across the installed fleet. The system also generates automated predictive maintenance reports, and the company backs the platform with human assistance 24/7.

In May 2026, AVIAN closed a $2.6M pre-seed round led by Founderful, the company's first external raise after operating profitably on a bootstrapped basis for two years. The capital will be used to expand engineering and deployment capacity, and to scale up operations beyond wood products into recycling, waste handling, maritime and mining.

Sources: AVIAN

Founders: Drew Hanover, Thomas Laengle


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Industrial thermal monitoringAI fire prevention manufacturingContinuous thermal monitoring platformIndustrial anomaly detection