Reduction in unplanned downtime on rotating equipment after predictive deployment.
Intelligent Systems for Production, Operations, and the Connected Factory.
Manufacturing is no longer a question of whether to digitise. It is a question of whether the systems being deployed are mature enough to run a production environment without becoming the next thing operations has to work around.
Entiovi works with manufacturers, process plants, industrial operators, and Industry 4.0 platform builders to engineer AI, data, and connected systems that perform where they are installed - on the line, on the asset, on the edge, and inside the operating environments the plant already runs.
Industry 4.0 has matured into a discipline. The early phase - sensors, dashboards, pilot projects - has given way to a harder problem: turning industrial data into reliable operational intelligence at the scale of a real plant. That requires AI engineering that respects machine reality, data platforms that handle high-volume telemetry without falling over, and integration with the OT systems that have been running the floor for years.
Entiovi's manufacturing practice is built around that engineering reality. Production-grade AI for production environments - not lab demonstrations dressed up for the floor. Models are validated on real plant data, deployed inside the OT and IT environment as it exists, and operated against outcomes that matter to plant managers, reliability engineers, and quality leads.
The plant is the design centre. The intelligence layer is engineered around it - not the other way around.
Reduction in unplanned downtime on rotating equipment after predictive deployment.
Increase in defect catch rate on a high-speed packaging line using edge vision.
Uptime achieved on a multi-site connected operations platform.
Annual yield improvement projected from a single-site quality intelligence rollout.
Manufacturing engagements span seven engineering capability themes, each developed as a stand-alone practice and each engineered to work alongside the others.
Asset health intelligence applied where failures actually originate.
Predictive maintenance, anomaly detection, and operational continuity engineering across rotating equipment, process units, and production lines. Built on real failure history - not theoretical degradation curves.
Visual inspection at line speed, engineered for the floor.
AI-powered visual inspection, defect detection, dimensional verification, and intelligent monitoring of the production line - engineered for the lighting, the speed, and the operating conditions of the line it serves.
The connected layer underneath everything else.
Connected machine telemetry, edge analytics, real-time event processing, and the data fabric that makes the rest possible. Designed for mixed protocols, legacy equipment, and the operational realities of the shop floor.
Historian-grade engineering for time-series at industrial scale.
Historian integration, time-series engineering, contextualised production data, and platforms designed for the volume, velocity, and variety industrial systems generate.
Real-time visibility tied to the levers that move OEE.
Real-time analytics, KPI engineering, OEE intelligence, and decision support grounded in live production and maintenance reality.
Intelligence on top of MES, SCADA, DCS, and PLC environments.
Analytics and AI layered on top of the systems that already run the plant - without disrupting them. Engineering that respects the OT side of the house.
Full-stack platforms for operators, OEMs, and Industry 4.0 product builders.
Custom platforms for plant operators, industrial OEMs, system builders, and Industry 4.0 product companies - with the AI, data, and integration layers designed in from the start.
Industrial technology investments are evaluated against the operating P&L of the plant. Entiovi's engagements are scoped against those outcomes from the first conversation.
Through asset health intelligence applied where failures actually originate.
Through visual and process-driven quality intelligence.
Through real-time visibility into the operational levers that actually move it.
Without lowering equipment reliability or coverage.
On the shop floor and in the control room.
That compounds in value as more lines, sites, and assets are brought online.
Engagements span the full surface of industrial operations - from the asset to the line, from the plant floor to the multi-site operations function.
Manufacturing engagements follow a structured arc that places operations, reliability, and quality stakeholders alongside the engineering team from the first week.
Equipment landscape, failure modes that matter, OT and IT systems, and the operational rhythm the engagement has to fit inside. Output is a prioritised opportunity map.
Sensor and data architecture, integration with historians, SCADA, MES, and DCS, edge and cloud topology, and the model and analytics design. Reviewed with reliability, quality, and IT/OT leadership.
Engineering with operations, maintenance, and quality teams alongside. Models validated on real production data - not synthetic benchmarks. Phased pilot deployment on a single line or asset class.
Live operation inside the plant, with the analytics surfaced where operators and engineers already work - control rooms, CMMS, mobile dashboards. Handover engineered with runbooks and operational ownership defined.
Monitoring, retraining, and capability extension as new lines, assets, and sites come online. Industrial AI is not a one-time install - it is an operating system that grows with the operation.
Entiovi's manufacturing practice is delivered across three core engineering capabilities. Each is a stand-alone enterprise capability - and each is engineered to integrate with the others where the engagement requires it.
Whether the priority is asset reliability, quality on the line, or the connected data layer underneath both - Entiovi's team will assess what is feasible, what is valuable, and what the right first build looks like inside the operational environment the client actually runs.