Industries We Serve
Industries — Infinite Uptime
PlantOS™ Vertical AI Platform

Vertical AI for Heavy Manufacturing Industries

Proven across 9 industries. Industry-specific fault libraries turn diagnostics into operator- validated outcomes.

PlantOS™ is a Vertical AI platform for heavy manufacturing. It runs industry-specific fault libraries, so the AI understands the process-induced fault signatures, not just generic vibration and alerts. PlantOS™ supports more than 946 plants across 26 countries, helping manufacturers improve uptime, reduce cost/ton, and increase throughput.

Click on any industry to see the equipment we cover.
The Categorical Error of Generic AI

Why does each industry need a different Vertical AI model?

The same motor fails differently in a steel mill than in a paper dryer, because the process around it is different. A generic model reads vibration alone and misses that context. Vertical AI reads the process conditions that actually drive the failure. That is why PlantOS™ runs a different model for each industry, not one generic model for all.

Industry Generic AI sees PlantOS™ also reads (and prescribes on)
Steel Vibration and temperature on the bearing Rolling loads, casting temperatures, and speed, catching hot-strip thermal fatigue, ladle heat-profile drift, and roll thermal overload
Cement Vibration on the kiln drive Kiln process swings and coating conditions, catching kiln ring formation, cyclone coating buildup, and girth-gear stress
Metals & Mining Vibration on the mill or crusher Ore-hardness and mill-load variation, catching SAG and ball-mill load swings, crusher impact loads, and conveyor wear
Pulp & Paper Vibration on the roll Moisture, felt tension, and steam load, catching wet-end web-tension drift and dryer-cylinder faults
Chemicals & Fertilizer Vibration on the pump or compressor Pressure, temperature, and corrosion conditions, catching coupling thermal loads, cavitation, and seal faults
Tire & Rubber Vibration on the mixer or extruder Mixing temperatures and extrusion variability, catching mixer thermal and torque stress and extruder-screw wear
Energy & Oil & Gas Vibration on the turbine or pump Steam load and efficiency drift, catching turbine efficiency loss and boiler-feed-pump stress
Food & Beverage Vibration on the line motor Continuous-production variability and washdown moisture, catching filling-line wear and moisture ingress
Pharma & Personal Care Vibration on the mixer or press Batch and cleanroom conditions, catching high-shear-mixer impeller wear, tablet-press stress, and HVAC drift

Frequently Asked Questions

01 What industries does PlantOS™ support?

PlantOS™ supports nine heavy manufacturing industries: steel, cement, metals and mining, pulp and paper, chemicals and fertilizer, tire and rubber, energy and oil and gas, food and beverage, and pharma and personal care. It is deployed across more than 946 plants in 26 countries.

02 Why do different manufacturing industries need different AI?

Because the process around a machine determines how it fails. A motor in a cement kiln faces thermal cycling and coating buildup, while the same motor in a paper dryer faces moisture and felt tension. Generic AI reads vibration alone and misses these. PlantOS™ runs industry-specific fault libraries that read process context, so it catches process-induced failures generic models cannot see.

03 How is PlantOS™ different for each industry?

Each industry gets its own fault library, trained on the failure signatures of that sector's critical equipment. For steel that means hot-strip thermal fatigue and roll thermal overload; for cement, kiln ring formation and cyclone coating buildup; for paper, wet-end web-tension drift. The underlying platform is the same, but what it looks for is tuned to each process.

04 Can one platform support multiple plants and multiple industries?

Yes. PlantOS™ supports more than 946 plants across 26 countries from one platform, including manufacturers that run several industries at once. Each site and each asset gets a model matched to its own process, while learning is shared across the installed base.

05 How does PlantOS™ adapt to different production processes?

PlantOS™ reads live process data, such as temperature, load, and speed, alongside vibration. It correlates the two using Dynamic FMEA, which re-ranks failure modes against actual operating conditions. This is what lets one platform adapt from a continuous steel caster to a batch pharma process without a generic one-model-fits-all approach.

06 Which manufacturing industries benefit most from prescriptive AI?

The biggest gains come in energy-intensive, downtime-sensitive industries where a single failure is very expensive: steel, cement, metals and mining, pulp and paper, chemicals and fertilizer, tire and rubber, energy and oil and gas, food and beverage, and pharma and personal care, where one stoppage can cost millions and process conditions drive most failures.