Manufacturing teams rarely lack data. The challenge is finding the right context fast enough — across ERP, MES, documents, machine data and workflows.
mAi helps users ask operational questions in natural language, retrieve relevant information from connected sources, and trigger actions in existing systems.
Understand
Understand context.
mAi interprets priorities, constraints, dependencies and user intent.
Find
Find the right knowledge.
mAi retrieves information from docs, ERP/MES data, machine data and historical context.
Act
Trigger the next step.
mAi can initiate workflows, update statuses, call APIs or notify the right people.
Example use cases
Quality management
"Has this issue been documented before?"
mAi searches QMS documentation, ISO procedures and customer complaints to retrieve relevant info and support consistent quality processes.
Process deviations
“Why did this batch behave differently?”
mAi connects production history, process data and quality records to support faster root-cause understanding.
Maintenance support
“How do I fix error code E-421?”
mAi retrieves the relevant manual section and compares it with past incidents.
Order delays
“Why is this order delayed?”
mAi checks machine status, production data and related dependencies — then suggests or triggers the next step.
Quoting & configuration
“Create a quote for this configuration.”
mAi can support product configuration, pricing, document generation and customer communication through connected workflows.
More than answers. Connected execution.
mAi combines three capabilities: reasoning, knowledge access and execution.
It can understand what the user is asking, bring the right system and document data into context, and carry out actions in connected tools — from mHub and mVariants to ERP systems, databases and services.
Designed for real production environments
mAi works with existing systems and workflows — without replacing what already runs. Depending on your needs, it can be deployed in the cloud or locally.
Integration
Works with existing systems — no rip-and-replace
Integrates into existing production workflows
Connects ERP, MES, documents, databases and APIs
Easy Adoption
No need to restructure processes before starting
Suitable for step-by-step implementation
Security & Privacy
Cloud or local deployment
Secure infrastructure and data protection
Role-based permissions
Start with one real process
mAi does not need to begin as a large AI transformation project. The best starting point is one concrete operational use case: planning, maintenance, traceability, quoting or another process where context is difficult to access today.
1
Choose a first use case
Select one process where information, context or execution creates friction in daily operations.
2
Connect relevant sources
Define the systems, documents, databases, APIs, and field and sales reports needed for your use case.
3
Decide deployment
Choose cloud or local deployment based on your requirements for privacy, control and scalability.
4
Test with real users
Validate mAi using practical questions and workflows from your day-to-day operations.
Start small. Expand when you're ready.
Starting with one project keeps risk low and delivers value quickly. When you're ready to expand,
we're here to help.
Together, we identify further opportunities for AI across planning, production, maintenance, quality, logistics, and sales—regardless of whether those processes run on Nuveon software or
existing third-party systems.