About
Building intelligence for logistics.
We build our own AI models to read logistics operations, understand them and deliver results.
- Models working on your operation
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- Capabilities, from diagnosis to running the operation
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- Industries we work in
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Our story
The same problem kept appearing across logistics operations.
Operators had no shortage of data. Performance, workforce, fleet, service, capacity and operational records were spread across different systems. What was missing was the intelligence to understand what mattered, why conditions were changing and what should happen next.
That led to a bigger idea.
The future of logistics will not be built around more dashboards. It will be built around intelligence that can understand operations, reason across them and help people make better decisions.
So we built Wehand as an AI research and deployment company for logistics.
Our intelligence model, Depot-1, reads an operation and works out what is driving its performance and where attention should go next.
Our agentic model, Operator-1, then carries out the work that turns those findings into results, with people in control of every decision that matters.
What makes us different
Intelligence we build ourselves
We develop proprietary AI models for logistics. Depot-1 understands operations and finds what should change. Operator-1 does the work to change it.
Results, not another system
We do not sell software for your team to run. We work with the reports and systems you already have, and deliver the results: higher carrier tiers, a better rate per mile, lower cost per drop.
Built to learn from outcomes
Every operation creates decisions, actions and results. Connecting those outcomes back to the conditions that produced them helps us build a deeper understanding of what works and continuously improve our intelligence.
Our values
Build for the real world
Logistics is complex, physical and constantly changing. We build around the realities of people, assets, demand, capacity and operational constraints.
Keep people in control
AI should strengthen operational judgement, not remove accountability. We design our systems around clear boundaries, human oversight and the ability to review important decisions.
Earn trust through evidence
Capability should be demonstrated rather than assumed. We evaluate how systems reason, where they fail and whether their recommendations lead to better outcomes.
Build AI safely
AI in logistics can influence people, vehicles and physical operations, so safety has to grow with capability. We research uncertainty, evaluation, oversight and how systems can take on greater responsibility responsibly.