Visual quality inspection
Defect detection at line speed on local hardware, with images and models staying inside the plant network. Central model management, local inference.
AI for Manufacturing & Industrial
Inference at the point of production — on the plant floor, connected or not, without collapsing the OT/IT boundary.
The constraint
The datacenter is in the wrong place
Manufacturing inverts the usual constraint. The problem is rarely that data is too sensitive to move — it is that moving it is too slow, too expensive, or impossible.
Latency and connectivity rule out round trips. A visual inspection decision on a line running at cycle speed cannot wait for a datacenter, let alone an API. Plants also lose connectivity, and production does not stop when they do.
OT and IT separation is a safety control, not an inconvenience. The Purdue-model boundary between production systems and corporate IT exists because a compromise on the OT side can injure people. Any AI architecture that quietly bridges it will be rejected by the people responsible for plant safety, and rightly so.
The result is a fleet problem: many small deployments at the edge, centrally managed, with a carefully controlled path back to the enterprise.
Applications
Where AI creates value on the plant floor
Predictive maintenance
Combining historian telemetry with maintenance history and manuals to flag developing failures and explain the reasoning to a technician.
SOP and procedure retrieval
Making standard operating procedures, work instructions, and equipment manuals answerable at the point of work rather than in a binder.
Technician assistance
Surfacing failure history, parts, and prior fixes for a specific asset — including on maintenance networks with no internet access.
Supply chain exception handling
Triaging shortages, delays, and quality holds against contracts and prior decisions, routing exceptions into ERP.
Compliance
Standards and constraints
| Framework | What it drives in the architecture |
|---|---|
| ISA/IEC 62443 | Industrial control system security. Governs zone and conduit design — which directly constrains where inference hardware may sit and what it may talk to. |
| ITAR / EAR | Applies where technical data is export-controlled, common in aerospace and defense supply chains. Rules out offshore or commercial cloud processing. |
| NIST CSF | Frequently the umbrella framework for OT-adjacent risk, and the language plant IT will expect findings in. |
| Safety instrumented systems | AI must never sit in a safety-instrumented function path. Advisory only, with the interlock logic untouched. |
We align AI governance to NIST AI RMF and ISO/IEC 42001 alongside your sector-specific obligations.
Integration
Integration with production systems
We integrate with SAP and other ERP, MES platforms, SCADA and process historians, and PLM systems. Edge nodes run optimized models sized for the available hardware, managed as a fleet with staged over-the-air updates. The path from OT to enterprise is one-directional and explicitly designed, so the segmentation your safety case depends on stays intact.
Questions
Frequently asked
Does this require new hardware on every line?
Usually some, but less than expected. Model optimization — quantization, distillation, compilation — often brings inference within reach of industrial PCs or modest GPU appliances you already have. We size it against the actual workload during the assessment.
What happens when the plant loses connectivity?
Edge deployments are designed offline-first. Inference continues locally; results queue and reconcile when the link returns. If losing connectivity stops production, the architecture is wrong.
Will this cross the OT/IT boundary?
Not in the direction that matters. Data flows outward under explicit control; nothing on the enterprise side initiates into the OT zone. The zone and conduit model is a design input from day one.
Related
Private AI
Production AI systems running entirely inside your infrastructure, with no third-party processor in the data path.
Private AIDeployment Models
Managed API, private cloud, on-premise, edge, and air-gapped compared — including where each one fails.
Compare modelsDiscuss your constraints.
The assessment evaluates your data, infrastructure, and regulatory position, then recommends a deployment architecture your review process can actually approve.
We will sign your NDA before a detailed technical discussion.