Field
How to prevent knowledge loss when an expert leaves
A maintenance expert is retiring: how to capture their expertise in six months, what to prioritise first, and the checklist from D-180 to D-0.
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Industrial maintenance AI agent: a definition
A maintenance AI agent is software that reasons about a fault to guide diagnosis and decision, beyond prediction. A clear definition.
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ERP or CMMS for maintenance: what are the differences?
ERP or CMMS for maintenance? The differences, how the two tools complement each other, coupling them, and making the most of what you already have.
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AI assistant for maintenance technicians: what works
An AI assistant for field maintenance technicians: market promises, what genuinely helps on site, and what is just marketing.
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Intervention reports by voice instead of typing
How AI automatically generates your intervention reports from voice dictation. Single entry, accessible diagrams, captured know-how.
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Preventive vs predictive maintenance: what's the difference?
Corrective, preventive, condition-based and predictive: the full picture of maintenance strategies, their limits, and how to choose.
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The 5 mistakes that kill an industrial AI project
A large share of industrial AI projects fall short of their goal. The 5 mistakes that kill them, and how to avoid them, on method and on the ground.
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Symptom or root cause: are you fixing the right problem?
Find out why treating the symptom without identifying the root cause is costly for your maintenance. Complete guide and concrete cases.
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The relational graph: mapping the machine from diagrams
A relational graph turns your electrical, pneumatic and hydraulic diagrams into a navigable map of components, for safer diagnosis.
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Digital twin or sensors: which to choose for maintenance?
A functional digital twin is built without sensors, from your diagrams. What sets it apart from sensor-based predictive maintenance, point by point.
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How AI models your equipment from electrical diagrams
AI modelling of industrial equipment turns your electrical diagrams into a functional digital twin, for a structured diagnosis.
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Functional digital twin: maintenance without sensors
The functional digital twin models your equipment as a navigable graph (components, diagrams, failures) for guided diagnosis, with no sensors and no 3D.
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An immune system for your machines against failures?
How a factory develops an immune system against failures: capturing every resolved incident to diagnose faster and anticipate.
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Field technicians, the real AI experts
Your field technicians are the real AI experts. How to capture their tacit knowledge and build a collective intelligence.
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AI-guided diagnosis in maintenance: the complete guide
Understanding AI-guided diagnosis in industrial maintenance: principles, how it differs from attached procedures, FMEA, MTTR. Complete 2026 guide.
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ChatGPT vs orchestrated AI in industrial maintenance
ChatGPT in industrial maintenance: useful for Q&A, limited for diagnosis. Comparison with an orchestrated multi-agent AI built for the plant.
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Capturing maintenance know-how: the complete guide
Your experts are retiring and taking their know-how with them. Methods to capture field know-how in maintenance before it disappears.
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Trustworthy AI for industrial maintenance: the guide
What is trustworthy AI for critical operations in industry? The 6 pillars, the AI Act, data quality and the concrete criteria to look for.
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Trustworthy AI is not perfect AI
Trustworthy AI is not perfect AI. Discover why transparency and human supervision are the keys to adoption in maintenance.
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Trustworthy maintenance AI starts with reliable data
Without reliable data, no trustworthy AI in industrial maintenance. Customer case: diagnosis cut from 3 hours to 15 minutes with Mimorian.
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Make the most of the field know-how MES forget
MES run production but overlook field knowledge. See how to capture technicians' expertise with Mimorian.
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Industrialising the collective memory of maintenance
Field knowledge is lost with every departure. See how to industrialise collective memory in maintenance with industrial intelligence.
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Recurring failures: the invisible cost of lost know-how
Recurring failures cost dearly for want of knowledge capture. How to structure field feedback so the same fault is never diagnosed twice.
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