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Manufacturing · Example pattern
Plant visibility and a maintenance copilot
Example pattern: machine health dashboards and a local AI copilot for operators, with all data staying on the plant network.
Example pattern, not a client case study. Outcomes describe what a well-scoped engagement aims for.
The challenge
Mid-size plants running mixed CNC and packaging lines often log downtime hours after it happens. Machine manuals sit as PDFs on shared drives, and maintenance is mostly reactive.
Managers lack a single live view of throughput and alerts. Sending machine data to public cloud LLMs is usually off the table, so any AI guidance has to run locally.
How we typically approach it
The delivery we would scope: a .NET ingestion layer that normalizes signals from different machine vendors into one plant dashboard, deployed on Kubernetes either in Azure or on-premises. Rule-based threshold alerts flag vibration and temperature anomalies for human review; we do not ship unverified "predictions" on day one.
A local AI workflow answers setup and troubleshooting questions from the ingested manuals, with citations to the exact page, and never leaves the plant network. Confirmed issues become maintenance work orders in the ERP.
Our approach
- 01
Machine signal inventory and adapters
- 02
Pilot dashboard for shift supervisors
- 03
Local RAG pilot over manuals on selected lines
- 04
Maintenance work-order handoff to the ERP
Target outcomes
Earlier visibility into stoppages
Troubleshooting answers cited from the manuals
Plant health on one screen
Data kept on-premises or in a private cloud
Less reliance on undocumented know-how
Typical technologies
.NETKubernetesAzureLocal LLMs
Related products
Discuss your project with us
Share your constraints and goals, and we will outline a realistic path from pilot to production.