Daniel Yun, Co-CEO of Willog
From warehouse floor to boardroom
Daniel Yun’s route into supply chain technology did not begin in a lab or a spreadsheet. It began in a logistics warehouse. Before founding Willog, he ran a traditional logistics operation and saw first-hand where shipments broke down and why customers lost faith in their carriers.
One problem kept recurring: when temperature-sensitive cargo, such as fresh food, was damaged in transit, there was no way to work out afterwards where or why it had happened. “The losses recurred, but we could judge the causes only through experience and guesswork rather than data,” Yun says. Most of what happened during a shipment’s journey simply vanished, unrecorded.
That gap is what pushed Yun to redirect his business towards logistics data. Willog built its own IoT sensor devices to capture trustworthy data at the source, then layered AI analytics on top to flag anomalies before they occur.
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The company has since extended that same data foundation into cargo insurance, aiming to connect logistics, AI and insurance on a single data layer. “It wasn’t a problem I observed from the outside, but one I lived through while running the business myself,” Yun says. “It was the problem I understood best, and therefore the one I was most confident I could solve.”
Making invisible cargo visible
Enterprise systems such as ERP, WMS and TMS are good at tracking what is being shipped, how much, and when. What they largely miss is the physical condition cargo actually travels in — temperature, humidity, light, shock, tilt. Yun describes this as a grey zone that sits outside conventional supply chain software.
Willog’s approach spans four stages. Its own IoT devices, branded Willog Safe, capture physical data at the point of sensing. That data is combined with external context, such as weather and route information, to anticipate problems. The system then prescribes what action should be taken, and finally preserves the entire sequence as verifiable evidence. Rather than simply showing where cargo is, Willog feeds physical-world data back into the enterprise systems that were missing it.
A case with a global e-commerce client illustrates what this looks like in practice. Digital-twin mapping was used to identify thermal weak spots inside a fulfilment centre, turning a problem the client had only vaguely sensed into concrete, location-specific data.
“Information at the level of ‘this warehouse has unstable temperature control’ isn’t enough to act on,” Yun explains. “But once it becomes clear which zone deviates from standards, under which conditions, and how repeatedly; that’s when it leads to real action.”
He describes the lesson as being less about proving a risk exists and more about making the data specific enough to drive a decision.
The zero churn structure
Willog reports zero per cent churn and 100 per cent contract renewal across 2024 and 2025, figures that stand out even against strong SaaS benchmarks. Yun attributes this to how deeply the system is embedded in a customer’s operations rather than sitting alongside them.
“If we were simply providing one more dashboard, a customer could switch away at any time,” he says. “But Willog is embedded in the customer’s own processes — inbound, outbound, quality control, and regulatory compliance.”
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Once a team has experienced catching problems before they happen, he argues, reverting to intuition-based decisions feels like a step backward. Leaving becomes difficult not because of contractual lock-in, but because the system has become part of how the organisation works.
Where the real moat lies
Real-time telemetry is becoming increasingly commoditised, with multiple providers now able to supply sensor data. Yun places Willog’s differentiation elsewhere, in the accumulated context around that data and the products built on top of it.
Over five years and across six industries, Willog has built up domain-specific knowledge of how different cargo types respond to particular conditions and where losses tend to occur.
The value, he says, comes from interpretation: “The same temperature reading only becomes valuable when you can interpret what it means for a specific pharmaceutical, and what it translates to as an insurance premium.” That combination of operational data and the ability to convert it into financial value, built up over time, is what he considers the company’s real barrier to entry.
Growing through references, not persuasion
Willog’s new contracts grew several-fold last year while customer acquisition cost fell, a shift Yun credits to reference-based expansion rather than any change in sales tactics. Early on, without a track record, approaching large enterprises and government agencies was difficult. The company instead built credibility steadily with small and mid-sized customers, and focused on passing the certifications demanded by its most exacting clients on quality and security.
That track record became a trust signal in its own right, generating inbound interest from companies that had seen it. “We shifted from a model where we approached and persuaded customers, to one where companies that had seen our references reached out to us first,” Yun says.
Trust in sectors that cannot afford mistakes
Willog’s deployments include biopharma cold chains and overseas military logistics, sectors where a single failure carries serious consequences and decision-makers are naturally cautious about new technology. Yun says conservative buyers are less interested in how advanced a system is than in whether failures can be explained afterwards. Willog’s AI judgments are kept traceable, with the underlying data preserved as evidence rather than treated as a black box.
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Sequencing mattered too. Rather than asking customers to trust an unproven AI system outright, Willog first cleared some of the strictest verification standards available — international transport for Corning, global knock-down transport quality management with Hyundai Glovis, and cold-chain transport for the ROK Army General Supply Depot. Passing military supply logistics vetting, in particular, gave the company more credibility with subsequent conservative clients than any pitch could.
From monitoring to insurability
Yun says the realisation that shipment data could underpin insurance came from recognising that proof of what actually happened in transit could be used to price risk by measurement rather than estimation. In this model, AI prediction and prevention reduce the probability of incidents occurring at all, while insurance, priced on measured data, covers whatever residual risk remains. “If prediction and prevention are the domain of reducing risk, insurance is the domain of taking responsibility for the risk that still remains,” he says.
What comes next
Willog’s roadmap includes further expansion into Europe and Southeast Asia, alongside a longer-term ambition to go public. Yun frames the IPO as a byproduct rather than the goal itself, contingent on sustaining reference-based growth internationally and establishing insurance as a genuine revenue line rather than a stated plan.
On Southeast Asia specifically, Yun pushes back on the idea that Willog is simply a sensor vendor. With regulation varying by country and cold-chain infrastructure maturity uneven across the region, he argues that knowing where cargo is isn’t sufficient — the value comes from pinpointing where and under what conditions losses occur, something Willog’s five years of cross-industry data is built to do.
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Looking further ahead, Yun describes the company’s ambition in structural terms: an infrastructure answering what physically happened, what is likely to happen next, and what that risk is worth, with data, AI and insurance interlocking on a single foundation. “We want to change the very grammar of the industry, from after-the-fact response to advance prediction and prevention,” he says.
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