01
Field-service copilot
Answers from equipment history and manuals, not a search box over a document pile.
Industries · Manufacturing
Manufacturing
The part, the fault code, and the last fix still live in people and PDFs.
Discuss a manufacturing problem →The pain, named
Field knowledge is still in people, PDFs, and plant memory.
Which part fits the model, what the fault code means, how the dealer fixed it last time. A technician or an agent cannot query that. Ground the workflow in equipment, parts, and service history before judging a chatbot over document search.
Use cases
Specific workflows,
in buyer language.
These are operating problems, not generic technology categories. Each one links to relevant experience only where the match is exact.
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Parts & BOM intelligence
Which part fits which machine, across models and revisions, without plant-by-plant reconciliation.
- 03
Supply-chain resilience
Which suppliers and lines fail together when a part goes short.
Selected experience
Related work,
clearly labeled.
These are anonymized experience examples, not client references or endorsements.
01
Global agricultural equipment manufacturer · full agentic AI deployment
Agents that could use connected equipment, parts, and business context instead of isolated system extracts.
Regulatory context
Requirements,
from primary sources.
EU AI Act
EU high-risk rules can apply when AI is a machinery safety component and third-party conformity assessment is required.
Regulation (EU) 2024/1689 ↗Start with the operating problem
Bring the decision,
workflow, or system.
The first conversation clarifies what must change, why it matters now, and what evidence would support the next decision.
Discuss the problem →