Introducing Depot-1

Depot-1 is our research into operational intelligence for logistics. The aim is to build systems that can take fragmented operational information, understand what is happening and identify where attention should be directed.

That means reasoning across areas such as driver performance, fleet availability, defects and disputes, service quality, cost, capacity, route data and management actions. The value is not simply in detecting that something changed, but in understanding why it changed, what matters most and what should happen next.

A useful operational model should be able to separate symptoms from underlying causes and prioritise the actions most likely to improve the outcome. That is the research problem behind Depot-1.

Decision-making and execution are different technical problems. A system can be good at diagnosing an operation without being ready to take action inside it.

Execution introduces a higher level of responsibility. Systems need to work within clear permissions, respond to changing conditions, recover from errors and know when control should return to a person. That is why Depot-1 focuses on understanding and reasoning first.

Logistics is already becoming more automated. Warehouses are becoming more robotic, vehicles are becoming more connected and software is taking on more coordination work that once sat entirely with people.

The next step is not simply more automation, but more intelligence inside that automation. Our research with Depot-1 is focused on that transition, from understanding operations to increasingly helping run them.