What equipment and systems can connect?
First check machine protocols, available signals and interfaces from MES, ERP and QMS. Define the product, lot and time records required for the initial scope.
Validate a focused scope. Expand into operations.
Review implementation, licensing, hardware, maintenance and funding-program collaboration.
Four questions before you start.
Will production need to stop?
Start with read-only collection and verify connection requirements for each machine. Schedule wiring or control changes in agreed maintenance windows; activate controls after separate validation.
What does our team prepare?
Production, quality and maintenance staff explain rules and exceptions. Ingkle ontology experts structure them using interviews and proprietary tools; staff validate them against actual practice.
What is the scope, and who operates it?
Begin with one problem and its equipment and records. Document permissions, approvers, allowed actions and incident handling; validate data, knowledge and AI responses before expanding.
Connect data and apply AI within one implementation
NAZARE
Connect the data AI needs
- Collect PLC and sensor signals from selected equipment
- Connect and store MES, ERP and QMS records
- Make expert criteria readable by AI
Build and validate together
NAVIS
Apply AI to the connected data
- Analyze causes using live signals and history
- Propose actions using operating criteria
- Execute after approval and verify results
Start using AI on the equipment connected first
Use AI before every machine is connected. Validate analysis and responses on the first connected equipment, then expand.
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Answers before you get started.
Review connectivity, product scope, existing systems, AI adoption and operations.
What server capacity does your site need?
Size for tags, rates, retention, concurrency and availability, not equipment count alone.
Choose a product configuration for your needs.
Choose a scope from data collection through operational analysis and AI agents.
Plan for operation after deployment.
Choose support for current-version stability or continued product improvements.
When your process needs a tailored prediction model.
Scope specialized prediction or classification models after reviewing data and validation requirements.
Make the initial scope clear.
Separate product supply, integration, data preparation and operating ownership.
Start with one problem before changing the whole factory.
Move through discovery, focused validation, operational handover and expansion.
Prepare for manufacturing AI with national and local government programs.
Explore Ingkle’s national R&D, manufacturing AI smart-factory and Cheonan–Chungnam programs, and identify an adoption path for your factory.
Start together
Tell us what you want to improve first.
Tell us about your equipment, data and priorities to review a path from connection to operations.