Sensors, predictions, premiums: How Willog turned shipment data into an insurance biz

Daniel Yun, Co-CEO of Willog, traces his journey into supply chain technology back to a traditional logistics operation. While running a logistics business, he observed that when temperature-sensitive cargo was damaged during transit, there was no way to determine the cause afterwards. This led Yun to shift his focus towards logistics data.

Willog developed its own IoT sensor devices to collect trustworthy data at the source and layered AI analytics on top to detect anomalies before they occur. The company then extended this data foundation into cargo insurance, aiming to connect logistics, AI, and insurance on a single data layer. Daniel Yun explains that most supply chain software tracks what is being shipped, how much, and when, but misses the physical condition of cargo during transit.

Willog's approach spans four stages, capturing physical data at the point of sensing, combining it with external context to anticipate problems, prescribing actions, and preserving the entire sequence as verifiable evidence. This data is then fed back into enterprise systems, turning vague insights into location-specific data. Willog reported zero per cent churn and 100 per cent contract renewal across 2024 and 2025, highlighting the deep integration of the system into customers' operations.

The company's real moat lies in the accumulated context around data and the products built on top of it, along with domain-specific knowledge of how different cargo types respond to conditions. Willog's deployments include biopharma cold chains and overseas military logistics, sectors where decision-makers are cautious about new technology.

The company's trust comes from traceable AI judgments and preserved evidence, demonstrating the value of interpreting temperature readings to determine insurance premiums.