Most manufacturers have run an AI pilot — a predictive-maintenance trial, a vision proof of concept, a demand-forecasting model. Few have one running every shift on the line. The hard part in 2026 is the same one that stops the others: connecting models to PLCs, SCADA, and MES, deploying them at the edge where latency and uptime are not negotiable, and earning the trust of an operations team that will not bet the line on a black box. We build industrial AI that ships to the floor — predictive maintenance, visual quality inspection, and scheduling that holds up against real machines and real downtime.
By 2026 most manufacturers have AI somewhere, but only a small minority have moved past single-use pilots to AI running at enterprise scale on the line. The reasons are structural: a plant is an operational-technology environment built for uptime and control, not for data science. The constraints below are not edge cases — they decide whether a model ever leaves the lab and reaches the shop floor.
Real-time floor AI — inline inspection, predictive maintenance — has to run at the edge, next to the machine, because the round trip to the cloud is too slow and the network too fragile to depend on. Bridging the operational-technology floor to the IT stack, usually through OPC-UA or a protocol-translation layer, routinely adds months to a deployment before a single prediction is made.
A plant runs on PLCs, SCADA, historians, and MES platforms that were optimised for monitoring and control, not for cross-system reasoning. Integration with these legacy systems and the data silos around them is the single most cited barrier to industrial AI adoption. The model is rarely the hard part; getting clean, aligned data out of the floor is.
A good line produces very few defects, which is exactly the problem for a vision model: thousands of acceptable parts for every flawed one, and rare failure modes it has barely seen. Training a reliable inspector means handling severe class imbalance — with synthetic generation, anomaly detection, and careful sampling — not just collecting more images.
On a production line, a false stop costs throughput and a missed defect costs a recall — and a model that interferes with a safety-rated control system is a non-starter. Industrial AI advises and inspects; it does not get to gamble with uptime or safety. Earning the right to act on the floor is a higher bar than passing a demo.
The engineers who know the machines are retiring, and the data scientists who know the models have never stood on a line. Few teams hold both at once. That gap is why so many pilots stall after the consultants leave — and why we build for handover, so your own people can run and extend what we ship.
Sensor streams, quality records, maintenance logs, and ERP data live in separate systems that were never designed to talk to each other. A single line can generate tens of gigabytes of sensor data a day, but if it cannot be captured, aligned, and trusted, none of it trains a model. Fragmented data is why pilots never scale.
Predictive maintenance and visual quality inspection are the two use cases manufacturers reach for first — and the two where the gap between a working trial and a system running on every shift is widest. The blockers are consistent: OT/IT integration, edge deployment, and data that was never captured cleanly enough to trust. We work exactly where that gap is, engineering industrial AI that survives contact with the line.
Built for the edge
Models that run next to the machine, at line speed, and degrade gracefully when the network or the cloud is not there.
You own it
Code, models, and documentation are yours, designed so your controls and engineering teams can operate and extend them after we leave.
We work on the use cases where manufacturers see real return and where the deployment realities — edge, OT integration, uptime — are non-negotiable. Each maps to a Miniml expertise team that has shipped it before, so the build, the deployment, and the handover come from people who have done it in a production environment.
Inspection that checks every part at line speed and catches surface defects, dimensional drift, and assembly errors that human eyes and legacy gauges miss — engineered to learn from the handful of defects a good line actually produces, not to demand thousands you do not have.
Computer vision →Models over vibration, temperature, current, and historian data that flag a failing asset before it stops the line, and forecast demand and capacity ahead of the schedule — tuned to give maintenance enough lead time to act without drowning them in false alarms.
Predictive analytics →Agents that turn a prediction into action — drafting the work order, checking parts inventory, and coordinating the technician, with a human approving the cases that matter. The closed-loop layer that moves a plant from prediction to response.
AI agents →Grounded assistants over equipment manuals, prior work orders, and parts databases — so a technician on the floor finds the right procedure and the right part fast, with a citation back to the source. Built to work at the edge, where the network will not always hold.
Knowledge & retrieval →Extraction that reads engineering specs, work orders, certificates of conformance, and supplier documentation accurately, flags the exceptions, and feeds clean data into MES and ERP — with provenance back to the source page for every field.
Document intelligence →Decisioning that adjusts the production plan against labour, equipment, energy cost, and changeover penalties to keep deliveries on time when reality shifts — with the reasons attached, so a planner can understand and override any recommendation.
Decision intelligence →On the floor, a model that cannot run at the edge, connect to the controls, or be trusted by operations is a model that never leaves the lab. We treat the realities below as part of the build — not problems handed to your team after the model is “done.”
Deployed next to the machine, at line speed, and resilient when connectivity is not.
Integrated with PLC, SCADA, historian, and MES data through standard industrial protocols.
Performance watched continuously, with alerts before a model degrades as the line and the product change.
Documentation and enablement so your engineering and controls teams own it after we leave.
Usually for three reasons: the model could not run at the edge where the line needs it, it never connected cleanly to the PLCs, SCADA, and MES, or the data was too fragmented to trust. Industrial AI fails on the floor, not in the demo. We work backwards from production — the edge deployment, the OT integration, and the data foundation — so the pilot is built to ship from day one.
Through the floor’s own standards — typically OPC-UA, MQTT, or a protocol-translation layer that reads from historians and controllers without sitting inside a safety-rated control loop. The AI layer advises and inspects; it does not take over the controls. We design the integration against your existing segmentation and security posture, so connecting the model does not open a new path into the control network.
Yes — that is the normal starting point, because a good line produces few defects. We handle the class imbalance directly: anomaly-detection approaches that learn what “good” looks like, synthetic generation of rare defect types, and sampling strategies built for skewed data. A reliable inspector comes from engineering around scarce data, not from waiting until you have collected thousands of flawed parts.
It has to. Real-time inspection and predictive maintenance cannot wait on a round trip to the cloud, and the network on a floor is not something to depend on. We deploy inference next to the machine, sized to run at line speed, and design it to degrade gracefully when connectivity drops — so the line keeps moving whether or not the data centre is answering.
No — by design. The AI layer reads from the floor and advises operations; it does not sit inside a safety-rated control loop or override a PLC. Predictions and inspections come with the reasons behind them and a clear override path, so a model can flag a failing bearing or a suspect part without ever being able to gamble with uptime or safety.
Yes. The code, the models, and the documentation are yours, designed so your engineering and controls teams can operate, retrain, and extend them independently. Enablement is part of the engagement — given the skills gap on most floors, we build for handover so the system keeps running long after we leave.
A 30-minute conversation with a senior consultant. Bring a model stuck in proof of concept, a vision or maintenance trial that never made it to the line, or an OT integration your team keeps hitting a wall on. We’ll tell you what it takes to get it running on the floor — at the edge, connected to your controls, and built for you to own.
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