01
Human-centered AI
Assistive systems for decisions that still belong to a person.
- Show uncertainty instead of hiding it
- Keep a path a supervisor can review
- Write the interface for the operator
Technology practice
Human-centered AI, warehouse automation, and the custom tools around them — built so the person doing the work can still explain the system.
Practice
Three kinds of work, held to the same rule: a person on the floor can explain what the system did.
01
Assistive systems for decisions that still belong to a person.
02
Software for storage, movement, and the exceptions machines should not swallow.
03
Narrow internal tools when a platform does not fit the job.
Approach
Principles for systems that share a building with people. They are a standard, not a case study.
01
Automation can prepare a decision. It does not get to own one.
02
The aisle, the shift, and the exception come before the model.
03
If a supervisor cannot describe what the system did, it is not finished.
04
Tools should join the floor, not ask the floor to reorganize around a demo.
05
Prefer a signal an operator can verify over a score nobody can audit.
Capabilities
Illustrative placeholders. These are not engagements, and no customers, metrics, or outcomes are claimed.
01
A station-side check that flags a mismatch and leaves the confirm with the operator.
Placeholder
02
A queue for the cases automation should hand back instead of forcing through.
Placeholder
03
A way to compare slot ideas against constraints a planner already uses.
Placeholder
04
Software that carries context from one crew to the next without a new ritual.
Placeholder
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