Zoniax does AI on both sides of the line. In software, we build custom models, data and ML pipelines, and the applications that put them to work. On the plant floor, we deploy vision inspection, predictive maintenance, and soft sensors that run at the edge — on real processing lines. One team, one engineering standard, signal to decision.
Real AI value sits between two skills that rarely live in one team: the software engineering behind a model, and the field engineering to run it on a live plant. Zoniax does both.
Custom models and the systems around them — the data, training, and applications that turn a model into something a user or an operator can actually rely on.
Intelligence that runs where the process does. Models trained on a plant's own data, deployed to the edge, and held to the constraints of real operations — not a benchmark.
Industrial AI only earns trust if it survives contact with the plant. Our loop is built for that — and it runs on the Zoniax Platform, so the path from raw signal to a model in production is one system, not a handoff.
Capture the signal — sensors, edge gateways, and clean pipelines that turn a noisy process into labelled, trustworthy data.
Train against the plant's own data — vision, timeseries, or tabular — with evaluation that reflects real operating conditions.
Package the model to run beside the asset — low-latency inference that keeps working when the network doesn't.
Monitor drift, keep an operator in the loop, and retrain as the process changes. A model in production is a living system.
A model that scores well in a notebook can still be useless on a live line. What separates the two is rarely the architecture — it's the data the model learned from, the way it was tested, and what happens to it after it ships. Those are the parts worth talking about.
There is no model without signal. A pilot's first milestone is live telemetry, not a prediction — you cannot train on a measurement the plant never took.
A pump on one line is not the same pump on another. Models are fitted to the unit that runs them, at the resolution the process actually varies.
The test that matters is not a random split. It's whether the model would have called the event in time, under the conditions the line actually runs in.
Processes move. A recipe changes, the feedstock changes, a bearing is replaced. The model that was right in March is not automatically right in November.
Models that operate on your plant are trained from your plant's data, scoped to your organization. We do not aggregate operational data into shared training corpora without an explicit research agreement. And where training runs is a deployment decision, not a default: cloud, on-premises, or a hybrid where sensitive telemetry stays inside the plant and only aggregated data crosses the boundary — selectable per channel and per asset.
These three are where we've gone deepest — examples, not limits. The techniques are industry-agnostic and carry to any operation; what changes is the process they're pointed at and the limits they have to respect.
Vision inspection on fill and packaging, cleaning-in-place verification, and anomaly detection across mixing and pasteurization.
Combustion optimization, emissions soft sensors, and turbine-vibration forecasting — pushing output without crossing regulatory limits.
Surface-defect vision, spindle and tool-wear prediction, and closed-loop process optimization driven from the cell.
We publish our working notes on industrial and software AI — the techniques we trust, the trade-offs we weigh, and the approaches that didn't pan out. It's the evidence layer behind this page: see how we reason before you ask us to reason about your operation.
Tell us the problem — a model you need built, or a line you need to see clearly. We'll tell you plainly whether AI is the right tool, and how we'd ship it.