Engineering demonstrator and delivery pattern
Private AI for TwinCAT Virtual Commissioning and Engineering Support
A private engineering-assistant architecture grounded in approved TwinCAT source, alarms, manuals, tests and commissioning evidence, combined with simulation and human approval.
Situation
The engineering context
Controls teams need faster access to project knowledge and better test coverage, but production source, customer documentation and machine data cannot be sent indiscriminately to public AI services.
Approach
How the work was structured
- Index approved source, design documents, alarms, tests and support records
- Use private retrieval and model serving behind the organisation’s boundary
- Connect code understanding to simulated I/O, sequences and failure-path tests
- Require review and version-controlled acceptance of generated engineering changes
- Monitor retrieval quality, model behaviour, test results and operational use
- Retain rollback, audit and escalation to an accountable engineer
Evidence
Reviewable deliverables
- Use-case, data and risk assessment
- Private AI, retrieval and integration architecture
- TwinCAT knowledge and virtual-test pilot
- Evaluation cases, provenance and acceptance measures
- Governance, security, change and support procedures
Outcome
A controlled basis for the next decision
The pattern shows how AI can accelerate navigation, diagnosis, documentation and test preparation without granting it control authority. It provides a bounded route from a useful engineering pilot to an auditable production service.
Apply the approach