Manufacturing & quality
Work instructions, inspection findings and defect-resolution histories, connected to the decisions they inform.
The next generation of AI
needs the knowledge
behind industry.
Turn operational experience into a foundation for industrial AI. Source the workflows, technical knowledge and outcome-linked records your models need to understand how work gets done.
Discuss your initiative →The most useful knowledge
is often closest to the work.
A fault report. A technician’s diagnosis. An inspection that changes the next production run. Industrial experience lives across systems and teams. The opportunity is to connect it into examples an AI system can learn from—and be tested against.
Built around real operating environments.
Maintenance & field service
Troubleshooting sequences, service notes and repair outcomes that preserve the reasoning behind an intervention.
Logistics & operations
Dispatch decisions, fulfillment exceptions and process histories that show how teams respond when plans change.
A record shows what happened.
A workflow explains how.
Connect the situation, the action and the outcome. Preserve the context that makes operational experience useful.
Situation→Action→Outcome
A clear path from requirements to evaluation.
Identify the task
Define the environment, process and model capability you want to develop or evaluate.
Source the experience
Explore relevant businesses, record types and the context needed to interpret them.
Prepare for evaluation
Agree structure, anonymization, review and acceptance criteria before moving into a larger project.
Bring us the problem
your model needs to understand.
Share the industry, target task and data requirements. For visual demonstrations, include the environment and capture needs so feasibility can be assessed with potential partners.