Zoniax brings sensor-grade visibility, edge telemetry, and learning models to processing plants. Every reading observed. Every anomaly traced. Every decision documented.
We pair hardened physical instrumentation with online learning models so plant teams move from interpreting screens to acting on them.
Industrial-grade hardware engineered for the dust, vibration, and heat of real processing lines. Edge gateways stream readings to the platform at the cadence your operation demands.
Online models continuously fit themselves to your plant's normal — flagging drift, recommending interventions, and learning from each operator decision they observe.
Operators see lines, assets, and signals together. Alerts arrive with context. Investigations are one click from raw timeseries.
Line A — Process temperature, 6h
12s sampleEvent log — last 60 min
Sample data — your console mirrors your plant taxonomy.
We deploy in operations where downtime is measured in product, not minutes. The three below are simply where we work most today — the stack is industry-agnostic: ruggedized sensors, edge telemetry, and learning models apply wherever a process produces data.
Cold-chain integrity, fermentation control, cleaning-in-place verification, allergen-line traceability. Compliance reports generated from raw telemetry, not spreadsheets.
Continuous emissions reporting, combustion optimization, fuel-feed characterization, turbine vibration trending. Optimize output without crossing regulatory limits.
Spindle wear forecasting, surface-quality scoring, cutting-fluid characterization. Closed-loop signals from the cell to the production planner.
Don't see your sector? These three are examples, not limits. If a process has pumps, ovens, mixers, motors, or meters, it produces signals we can capture, model, and act on — the same stack, pointed at your operation.
The shape of Zoniax is deliberate: ingest at the edge, reason where it matters, integrate where you already work.
MQTT & HTTPS endpoints, schema registry, replay buffers — reliable capture before anything else.
WASM-based rule modules execute milliseconds from the source — alerts and overrides without round-trips.
SSO, fine-grained RBAC, signed audit trails. Plant, line, asset — every action attributed.
Native connectors for the ERP, MES, CMMS, and data lake you already run. No replatform required.
No. Most plants already have instrumentation that's serviceable; we instrument the gaps and bridge what's there. New sensors are only specified where coverage is missing or where existing hardware no longer meets the duty.
Only if your deployment opts into the SaaS console. On-premises and air-gapped hybrid deployments are first-class: the same platform, with telemetry that never leaves your network and updates delivered as signed bundles.
Capturing live telemetry is the first milestone — a pilot is scoped to start producing data early, before any modelling. Anomaly detection follows once there is enough baseline data to learn from, and predictive-maintenance signals become reliable after an asset has been observed across a full maintenance cycle. We agree the specific milestones and timeline with you when the pilot is scoped.
Modbus (RTU and TCP), OPC-UA, MQTT, BLE, LoRaWAN, and a small number of vendor-specific industrial buses. New protocol adapters are written in Rust and deployed as signed WASM modules.
Models that operate on a plant are trained from that plant's data, scoped to that organization. We do not aggregate customer data into shared training corpora without an explicit research agreement.
Enough to have seen the process behave normally, and enough to have seen it go wrong. That is why a pilot captures live telemetry before it models anything: anomaly detection follows once there is a baseline to learn from, and failure prediction becomes trustworthy only after an asset has been observed across a full maintenance cycle. We would rather scope those milestones with you than quote a number that sounds precise and isn't.
Not to begin. Anomaly detection learns a plant's normal without labels, which is usually the first model worth having. Labels make everything downstream sharper, so we build the labeling into the operator's workflow — operator-in-the-loop, with a full audit history — rather than asking you to hand over a labelled dataset up front.
Where training runs is part of the deployment decision, not a default. On-premises and air-gapped deployments keep telemetry inside your network. The common middle ground is hybrid: sensitive telemetry stays on-prem while aggregated data and model training run in the cloud, selectable per channel and per asset.
It goes stale — which is the normal condition of a model on a live plant, not a failure. A recipe change, a new feedstock, a replaced bearing: each shifts the baseline the model learned. Drift detection runs beside the asset rather than in the cloud, and retraining is then a decision with evidence behind it rather than a job on a schedule.
We'll meet your operations and process engineering teams, understand the lines that matter, and propose a pilot that ships measurable signals in weeks — not quarters.