The Prescriptive AI Platform Behind
Every Outcome.

PlantOS™ is a
prescriptive AI platform for heavy manufacturing. It transforms equipment,
process, and maintenance data into validated maintenance prescriptions, telling operators the
exact component at fault, the corrective action, and the deadline. Built as Vertical AI, PlantOS™
works with your existing sensors, control systems, and CMMS, so there is no rip-and-replace
and no guesswork.

Try PlantOS™


Download Trust Architecture Whitepaper

What does the PlantOS™ platform do?

Watch a quick walkthrough of the platform in action, from the main dashboard through the core
features and user workflow.
The video demonstrates how to navigate the interface, complete
common tasks, and get the most out of the system with a simple, step-by-step overview. As an industrial analytics platform, PlantOS™ brings equipment, process, and maintenance data together in one place to provide a clear view of plant performance.

One platform, five layers, live today

PlantOS™ is built as five connected layers. Each layer feeds the next, so raw sensor data
becomes a validated, executed work order. The five layers are:

• Sense and Ingest. Captures condition data from every critical rotating
  asset using the broadest sensing portfolio on the market: wired, wireless,
  piezoelectric, MEMS, self-powered, and third-party sensors.
• Edge and Connectivity. Moves data securely from the plant floor to
  PlantOS™ through a universal edge gateway, with edge inferencing at the source.
• Platform and Execution. Structures raw data into intelligence, then
  turns AI prescriptions into executed, recorded work through CMMS integration and digital
  logbooks.
• Analytics and Prescription. The AI layer. Vertical AI models convert
  condition and process data into evidence-backed prescriptions, separating equipment
  faults from process-induced faults.
• Decision and Planning. The strategic layer. Turns intelligence into
  risk-aligned plant decisions, with Dynamic FMEA re-ranking every failure mode against
  live equipment behaviour.

T5

Decision & Planning

VALUE

The strategic layer. It turns condition and
process intelligence into defensible, risk-aligned plant
decisions, with Dynamic FMEA re-ranking every failure mode against live equipment
behaviour.

CLICK
TO READ MORE

Plant Fusion – Maintenance

Decision authority that continuously prescribes a dynamic,
risk-first PM
program.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• A decision authority that continuously prescribes a
  dynamic
  PM
  program and asset strategy
• Replaces static time-based plans with ISO-aligned,
  risk-first PM
  logic
• Gives Reliability, Maintenance, and Plant leadership
  defensible
  logic for what, how, and
  when to maintain
• Purpose: OPEX reduction with lower unmanaged risk

OUTCOMES DELIVERED

• 15–25% PM cost reduction via over-maintenance
  detection on
  low-risk assets
• 20–35% MTBF improvement driven by cross-asset
  correlation
• 5–10% planned replacement CAPEX deferral through
  better
  run/repair/replace decisions
• 3–7% total cost of ownership reduction across
  maintenance,
  spares, and risk
• ISO-aligned governance — every recommendation
  traceable with
  audit trail

THE PROBLEM

• Plants have sensors, APM, CMMS, ERP — yet still
  suffer
  surprises
  and over-maintenance
• Fixed OEM PM schedules are worst-case assumptions
  applied
  uniformly across asset classes
• Spreadsheet heuristics and calendar rules produce
  both over-
  and
  under-maintenance
• Leaders need defensible, risk-aligned logic — not
  one-size-fits-all schedules

HOW IT WORKS
Step 1
Ingests asset
registers,
failure history, and condition data
into a canonical foundation
Step 2
Runs CI, RBI,
RCA, RCM,
FMEA,
PM optimisation, LCC —
quantifying risk per failure mode
Step 3
Selects strategy
per
failure
mode: TBM, CBM, on-condition,
run-to-failure, or redesign
Step 4
AI layer gated by
rules —
cannot override safety or
governance thresholds
Step 5
Pushes approved
PM
changes
into CMMS/EAM with full audit
trails

Plant Fusion – Spares

Governed engine prescribing risk-aligned min/max levels and
ordering
triggers.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• A governed decision engine prescribing risk-aligned
  min/max
  levels and ordering triggers
• Replaces static ABC classifications and manual
  min/max rules
  with risk-fusion logic
• Fuses failure behaviour, lead times, and criticality
  into
  inventory decisions
• Eliminates over-stocking and critical stockouts
  simultaneously

OUTCOMES DELIVERED

• Zero critical stockouts on assets with active RUL
  tracking
  in
  Plant Fusion
• 20–40% reduction in emergency procurement and
  expedited
  logistics cost
• Safety-critical items remain mandatory — reductions
  only
  applied
  where risk is quantified
• Supplier reliability tracking — actual vs. promised
  lead
  time
  per vendor

THE PROBLEM

• ERP ABC/XYZ rules are cost-only — ignore risk,
  reliability,
  and
  lead time
• Over-stocking low-criticality parts ties up capital;
  under-stocking critical parts stops
  plants
• Emergency procurement and expedited logistics
  inflate OPEX
• Safety-critical inventory cannot be touched without
  governed
  approval

HOW IT WORKS
Step 1
Uses same risk
foundation
as
Plant Fusion — Maintenance
(PoF/CoF, MTBF/MTTR, lead time, ABC)
Step 2
Calculates
risk-aligned
stocking: mandatory safety-critical,
optimised production-critical,
run-to-vendor
low-risk
Step 3
AI models learn
actual
consumption, failure clusters, and
supplier lead time variability
Step 4
Automatically
adjusts
EAM/ERP
min/max levels with governed
approval

Plant Fusion – Decision

Fuses reliability physics with business plans, finance, energy,
and
spares into one decision.

AI
Orchestration

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• The ultimate orchestrator fusing reliability
  physics,
  production
  plans, financials, energy,
  and spares
• Provides unified decision-support for Capex/Opex
  allocation
  and
  risk governance
• Translates engineering health into business risk
  metrics ($)

OUTCOMES DELIVERED

• Unified executive cockpit comparing risk vs cost
  across
  plants
• Evidence-based CAPEX justification replacing
  historical
  guessing
• Continuous alignment of engineering action with
  corporate
  ESG and
  financial goals
• Board-ready reports generated dynamically in
  real-time

THE PROBLEM

• Engineering health (vibration, temperature)
  disconnected
  from
  business risk ($)
• Capex decisions based on history rather than actual
  asset
  risk
• ESG goals (energy, waste) conflict with production
  schedule
  without unified planning

HOW IT WORKS
Step 1
Fuses data from
Plant
Fusion
— Maintenance and Spares with ERP
financials
Step 2
Computes
Cost-Risk-Benefit for
each proposed reliability action
Step 3
Presents
scenarios (Run,
Repair, Replace, Redesign) with ROI
impact


T4

Analytics & Prescription

VALUE

The AI brain. Verticalized AI models converting
condition and process data into evidence-backed prescriptions, distinguishing
equipment-reliability from process-induced faults and mapping fault progression to the
right action window.

CLICK
TO READ MORE

Prescription Engine

AI engine converting vibration, condition signatures, process stress
signals, and
operational parameters into structured mechanical and process fault
prescriptions.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
WHAT IT IS

• The AI Prescription engine converting vibration,
  condition
  signatures, process-
  induced stress signals, and operational parameters into
  structured mechanical
  and process fault prescriptions
• Combines spectrum analysis, envelope detection,
  bearing/gear
  mesh analysis,
  process parameter correlation, and multi-variable energy
  and
  throughput
  modelling
• Four domain modules running simultaneously — Energy,
  Throughput, Yield, and
  Quality — layered on top of continuous mechanical health
  monitoring
• Outputs plain-language, machine-readable prescriptions
  with
  RUL and urgency
  class — covering equipment-borne faults, process-driven
  failure modes, and
  operational inefficiencies
• Issues specific, evidence-backed corrective actions like
  a
  clinical prescription —
  with quantified expected impact per action and
  historical
  evidence
• Feeds directly into Plant Fusion — Maintenance and AI
  Worx

OUTCOMES DELIVERED

• 18–25 days average advance warning vs. 7–10 day industry
  benchmark
• Zero manual interpretation — fault type, location, RUL,
  and
  urgency in every prescription
• Covers Mechanical 20+ specific faults including Bearing
  wear, Unbalance, Misalignment, Looseness, Cavitation,
  Resonance, Lubrication degradation, Gear-mesh anomalies
  etc
• Covers 20+ Process induced faults including Kiln ring
  formation, Thermal
  overstress, Cyclone coating buildup, Ladle heat profile
  drift, Hot-strip thermal
  fatigue, Wet-end web tension drift, Coupling thermal
  loads,
  Roll thermal
  overload etc
• Distinguishes between equipment-borne degradation and
  process-induced
  acceleration — so corrective action targets the right
  root
  cause
• Multi-variable correlation prevents single-sensor false
  positives and alert fatigue
• Every prescription includes deviation, action, expected
  impact, and historical
  evidence

THE PROBLEM

• Raw vibration spectra and process anomalies require
  expert
  interpretation –
  most plants can’t scale specialists across every asset
  or
  every domain
  simultaneously
• Manual fault diagnosis is slow, inconsistent, and misses
  subtle mechanical
  signatures – while process anomalies causing energy
  waste,
  yield loss, and
  quality failures go undetected before operators notice
  them
• Multi-variable correlations remain invisible in
  single-sensor dashboards – making
  it impossible to distinguish equipment-borne degradation
  from process-induced
  acceleration
• Without structured prescriptions, AI detections don’t
  convert into work orders –
  operators receive alerts, not evidence-backed actions
  with
  expected impact
• Single-sensor alerts cause false positives and alert
  fatigue
  – eroding trust in the
  system and delaying critical interventions
• RUL estimates are essential for run/repair/replace
  decisions
  – but meaningless
  without the process context driving the degradation rate

T3

Platform & Execution

INFRA

The operational layer. Process contextualization
structuring raw data into intelligence, while CMMS integration, third-party sensor
connectors, and digital logbooks turn AI prescriptions into executed, recorded work.

CLICK
TO READ MORE

Process Canvas

Operational intelligence platform converting plant historian data
into
structured live KPIs.

Process
&
Energy Efficiency

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• The operational intelligence platform that converts
  DCS/PLC/SCADA/historian data into
  structured live KPIs
• Delivers role-specific dashboards, AnalytiX workspace,
  and
  downtime attribution
• The mandatory intake layer for Process Prescript —
  prerequisite
  for AI process
  prescriptions
• Delivers value from day one during commissioning, before
  AI
  prescriptions activate

OUTCOMES DELIVERED

• Zero manual dashboard rebuild — live views update
  straight
  from
  historian
• One unified operational picture replacing multiple
  disconnected
  screens
• CEMLine vertical installer — pre-built cement KPI pack,
  deployment in days not weeks
• Multi-plant fleet benchmarking when deployed as part of
  Plant
  Enterprise
• Real-time KPI updates direct from DCS/historian — no
  manual
  extraction

THE PROBLEM

• Plants generate enormous DCS data but lack a layer
  converting it
  into role-appropriate
  intelligence
• Engineers spend 2+ hours daily manually rebuilding Excel
  dashboards
• Process Prescript cannot activate without the structured
  data
  baseline Canvas provides
• Multiple disconnected operational screens prevent a
  unified
  plant view

HOW IT WORKS
Step 1
Connects to DCS, PLC,
SCADA
historians (PI System, OSIsoft),
MES/LIMS via AI Integration
Step 2
Maps raw tags to
structured
KPI models — SHC, SPC, OEE,
throughput, energy intensity
Step 3
Live KPI computation
continuously — no scheduled refresh, no
manual trigger
Step 4
AnalytiX workspace
enables
drag-and-drop time-series
comparison for deviation diagnosis
Step 5
Downtime attribution
captures
cause codes building the
historical baseline for AI models

AI Worx

Maintenance execution layer closing the loop between AI prescriptions
and
real work.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• The maintenance execution layer of PlantOS™ — closing
  the
  loop
  between AI prescriptions
  and real work
• Converts Plant Fusion and Process Prescript outputs into
  governed, tracked work orders
• Native mobile app for field technicians with full audit
  trail
  and compliance support
• Coexistence layer — not a replacement for SAP PM or IBM
  Maximo

OUTCOMES DELIVERED

• 100% of AI prescriptions tracked to work order status —
  zero
  silent drop-offs
• Criticality-based auto-escalation ensures high-risk
  tasks
  never
  slip through
• Auditable trail for every PM change, technician action,
  and
  completion
• Execution feedback improves prescription quality with
  each
  cycle
• Multi-location support — manage work orders across
  multiple
  plant units from one instance

THE PROBLEM

• Most industrial AI programmes fail in the execution gap
  —
  recommendations lost in WhatsApp
  and Excel
• Without structured work order conversion, AI
  prescriptions
  remain suggestions
• Completion data must feed back into AI models for
  continuous
  improvement
• Plants without a strong CMMS need a native execution
  layer

HOW IT WORKS
Step 1
Approved Plant Fusion
PM
changes and Process Prescript
actions flow in as work order inputs
Step 2
Structured work
orders
generated with asset, task, priority,
duration, and safety instructions
Step 3
Distributed to
technicians
via mobile app with checklists and
asset history
Step 4
Completion data feeds
back
into Plant Fusion asset record and
AI model retraining
Step 5
Overdue
high-criticality
tasks auto-escalate based on Plant
Fusion risk level

DigiLogBook

Digital replacement for paper shift logs — structured handover and
field
observation capture.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• Digital replacement for paper shift logs — structured
  shift
  handover and field observation
  capture
• Mobile-first capture with photo, voice-to-text, and
  structured
  form entries
• Makes operator observations AI-accessible in PlantOS™
• Preserves institutional knowledge before retirement
  walks it
  out
  the door

OUTCOMES DELIVERED

• Zero paper logbooks — 100% of shift observations
  digitized
  and
  searchable
• Operator institutional memory preserved as AI-accessible
  context
• 20–30% improvement in Equipment Fault Prescription
  accuracy
  from
  operator observation
  context
• Reduced false positive prescriptions through
  human-in-the-loop
  context

THE PROBLEM

• Paper logbooks trap observations in unsearchable silos
• Critical operator context lost at retirement —
  institutional
  memory evaporates
• AI fault prescriptions without human context are less
  accurate

HOW IT WORKS
Step 1
Operators log shift
handovers,
inspections, safety walks, and field
observations on mobile
Step 2
Voice-to-text
transcribes
comments,
OCR captures nameplate/gauge data,
GPS verifies location
Step 3
NLP indexes comments,
tagging
specific failure modes and components

AI Integration

Integrates data directly into third party CMMS (SAP PM / IBM Maximo)
via
APIs, Webhooks, MCP.

AI
Orchestration

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• Integrates data directly into third-party CMMS (SAP PM /
  IBM
  Maximo)
• Seamless transfer of prescriptions and work orders via
  APIs,
  Webhooks, MCP
• Coexists with existing software without replacing core
  ERP
  investments

OUTCOMES DELIVERED

• Zero manual data entry between platforms — 100%
  automated
  synchronization
• Leverages existing EAM investments while injecting
  prescriptive intelligence
• Reduces integration timeline to weeks rather than months

THE PROBLEM

• Siloed systems prevent AI from initiating actual
  workflow
• Custom API integrations are expensive and take months to
  deploy
• Manual synchronization of work orders is error-prone

HOW IT WORKS
Step 1
PlantOS™ triggers a
prescription based on machine data
Step 2
AI Integration layer
translates the prescription into EAM format
Step 3
Pushes structured
work
requests directly into SAP/Maximo

SenseLink

Connects existing third party sensor systems to PlantOS™ EAM.

AI
Orchestration

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• A connective layer for third-party sensors and asset
  systems
• Unifies disparate telemetry streams into a single
  structured
  schema
• Feeds external condition data directly into the
  prescription
  loop

OUTCOMES DELIVERED

• Protects existing hardware capital expenditure
• Unified asset risk views including third-party
  vibration/temp nodes
• Ensures continuous condition updates without vendor
  lock-in

THE PROBLEM

• Plants have already invested in third-party sensor
  platforms
• Existing sensor data assets must feed the AI
  prescription
  loop, not sit isolated
• A unified fault prescription engine requires all
  condition
  data in one place

HOW IT WORKS
Step 1
Authenticates with
existing sensor platform accounts via
credentials or APIs
Step 2
Reads condition
signatures from third-party systems in
native
format
Step 3
Feeds signatures into
PlantOS™ Equipment Prescript alongside IU
sensor
data


T2

Edge & Connectivity

INFRA

The connective tissue. Secure, multi-protocol
transmission from plant floor to PlantOS™ via a universal edge gateway, with edge
inferencing and RPM-triggered edge spectroscopy processing data at the source.

CLICK
TO READ MORE

EdgeIU

Process Edge Intelligence Unit capturing temperature, pressure, and
energy
data via tag reading.

Process
&
Energy Efficiency

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• The Process Edge Intelligence Unit capturing
  temperature,
  pressure, and energy data via
  tag reading
• Three variants: EDGEIU-PLC, EDGEIU-DCS, and EDGEIU-Touch
• Fits seamlessly into any existing control architecture
• Secure, continuous data transmission into the PlantOS™
  collaborative AI ecosystem

OUTCOMES DELIVERED

• Multi-protocol support across every industrial asset
  class
• 100% technology onboarding with zero plant downtime
• Zero rip-and-replace deployments — existing control
  architecture
  fully preserved
• Secure, continuous data transmission validated for
  enterprise
  IT/OT requirements

THE PROBLEM

• PlantOS™ AI is only as good as the plant data it can
  securely
  access
• Most control architectures use proprietary protocols
  that
  block
  AI integration
• Plants resist rip-and-replace of existing PLC/DCS
  investments
• IT/OT security concerns block many conventional edge
  solutions

HOW IT WORKS
Step 1
Reads tags directly
from
existing PLC, DCS, or SCADA systems
Step 2
Three form factors
match
different control architecture types
Step 3
Multi-protocol
support
across
all major industrial protocols
Step 4
Encrypted, continuous
transmission to the PlantOS™ cloud and
on-premise stack
Step 5
Deploys without
interrupting
plant operations or control
logic


T1

Sense & Ingest

INFRA

The foundation layer. It captures complete
condition data from every critical rotating asset,
built on the market’s broadest sensing portfolio: wired, wireless, piezoelectric, MEMS,
self-powered, and third-party sensors.

CLICK
TO READ MORE

Critical Sensor Kit

Five purpose-built sensors providing complete critical rotating asset
coverage.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• Five purpose-built sensors providing complete critical
  rotating
  asset coverage
• Each sensor matched to a specific deployment context,
  machine
  class, or criticality
  profile
• Feeds raw vibration, temperature, and RPM data directly
  into
  PlantOS™ Equipment Prescript
• Covers virtually all industrial rotating equipment
  scenarios
  from one integrated kit

OUTCOMES DELIVERED

• 100% critical asset visibility from day one — no asset
  left
  unmonitored
• <2 hours per asset commissioning with validated
  signal
  quality at installation
• Sub-harmonic fault detection weeks before breakdown via
  high-resolution spectrum analysis
• RPM-normalised analysis for VFD-driven assets — no
  external
  tachometer required
• Every detection feeds Plant Fusion — Maintenance for
  automatic
  risk re-assessment

THE PROBLEM

• Most plants have patchy sensor coverage — critical
  assets
  remain
  blind spots
• Wired sensors impractical for retrofit; wireless often
  lacks
  resolution for fault
  classification
• Hazardous zones and high-temperature assets typically
  excluded
  from monitoring
• Without high-resolution spectrum data, AI fault
  classification
  is unreliable

HOW IT WORKS
Step 1
vEdge 3XTURPM —
Wired,
12,800
LOR spectral resolution for
highest-criticality assets
Step 2
FitMachine 3XT / 3XT
Ex —
Battery-powered wireless MEMS (2–5
year life), ATEX variant for hazardous zones
Step 3
vSense 1XT —
Piezoelectric
sensor, -40 to 130°C, RPM range
2–3,900
Step 4
vSense 3XTRPM —
Dual-channel
DIN-rail edge node monitoring 2
machine points simultaneously
Step 5
All five feed a
single
Equipment Prescript engine for unified
fault classification

IDRS Managed Diagnostics

Managed reliability service combining offline CBM, online oil
monitoring,
and
RCFA.

Prescriptive
Maintenance

What it is &
Outcomes
Why it’s needed
How it works
WHAT IT IS

• A managed reliability service combining offline CBM,
  online
  oil
  monitoring, and Root Cause
  Failure Analysis
• Integrates six complementary diagnostic technologies
  into
  one
  unified service
• Delivers structured condition data directly into
  PlantOS™ as
  inputs for Plant Fusion —
  Maintenance
• Field execution by certified partner network and/or
  customer
  personnel

OUTCOMES DELIVERED

• Six technologies: oil analysis, thermography, manual
  vibration,
  ultrasonic, NDT, MCSA
• 48-hour SLA for RCFA reports — prescriptive, not just
  descriptive
• 100% of assets receive severity class at commissioning —
  mandatory for AI Worx triage
• Online oil monitoring tracks contamination and wear
  metals
  between scheduled changes
• Post-RCFA findings automatically update PlantOS™ PM
  optimisation
  logic

THE PROBLEM

• Many critical assets cannot be continuously monitored
  due to
  environment, access, or cost
  constraints
• Fills the coverage gap with structured periodic
  diagnostics
  and
  managed RCFA
• Provides the condition data Plant Fusion requires for
  risk-aligned PM decisions
• Eliminates the need for plant-side vibration analyst
  headcount
  during ramp-up

HOW IT WORKS
Step 1
Structured
measurement
routes
defined per asset by
criticality and failure mode
Step 2
Periodic field data
collection via calibrated diagnostic
instruments
Step 3
Oil samples analysed
for
contamination, wear metals, and
degradation trends
Step 4
RCFA performed on
failed
components — root cause identified
and prevention built into PM strategy
Step 5
All findings feed the
PlantOS™ asset register and update
Plant Fusion failure mode libraries

Five ways the plant talks to PlantOS™

Hardware-in. OT-in. Third-party
sensors-in. Enterprise systems bidirectional. Agents outbound via MCP.

Hardware data —
Input
IN
LIVE
1
EQSense
Ingests every Piezo sensor on-site — high-frequency spectral feed.

Agent prescriptions —
Output
OUT
5
MCP Server
Every AI agent (SAP Joule, GSI scheduler) calls IU via MCP.

Third-party Software —
Input
IN
4
Senselink
Third-party MEMS sensor data integrated into PlantOS™.

Enterprise
Bidirectional

LIVE
3
API
SAP PM · IBM Maximo · Oracle EAM. Prescriptions out → work orders.

OT data —
Input
IN
LIVE
2
Edge IU
Edge process data from DCS / SCADA / PLC integrated into PlantOS™.

How does PlantOS™ integrate with SAP PM, IBM Maximo, and Oracle EAM?

PlantOS™ connects to a plant in five
ways: it ingests piezoelectric sensor data through EQSense, edge
process data from DCS, SCADA, and PLC systems through EdgeIU, and third-party MEMS sensor data through
SenseLink. It exchanges work orders bidirectionally with SAP PM, IBM Maximo, and Oracle EAM through its
API, and it serves prescriptions outbound to AI agents such as SAP Joule through an MCP server.

The Infinite Uptime Difference

Structural advantages no generic AI platform can replicate with compute alone.

KILN MAIN DRIVE · DE BEARING · LIVE

Generic AI
PlantOS™

Reading
Vibration
Temperature
Raw speed
Also monitored
Tension
Torque
Current
Load

On a kiln main drive bearing, a generic model alerts at roughly 4 hours before failure. PlantOS™
prescribes at roughly 64 hours before failure, a 60-hour advantage.

Why does vertical AI prescribe 60 hours before generic AI alerts?

The same equipment can be read two ways. A generic model watches vibration alone. It sees a
clean trace until the mechanical spike, then alerts a few hours before failure. A vertical model
reads that same vibration against temperature drift and speed instability at peak coating-buildup
risk, and prescribes the corrective action about 60 hours earlier. Process context does not just
add data. It rewrites the fault logic the model applies.

Mechanical

Read by every model

Bearing wear
Unbalance
Misalignment
Looseness
Resonance
Gear-mesh
×

Process context

Read only by Vertical AI

Kiln ring formation
Coating buildup
Ladle heat drift
Hot-strip fatigue
Web tension drift
Roll thermal load
=
Partial
coverage
Reliability

The Categorical Error of Generic AI

Generic AI vs PlantOS™ Context-Aware Vertical
AI: why the same motor demands different models in a kiln, a rolling mill, and a paper
dryer.

Generic AI

One Model for Every Motor

Treats a motor the same way everywhere.
Result: Pattern-matching applied
indiscriminately.

Accuracy Stagnates or Deteriorates
• False Alarms
• Missed Faults
• Cold-Start Problem
Months of Zero-Value Learning

PlantOS™ Context-Aware AI

Motor in Kiln
Extreme thermal cycling & torque variability.
Motor in Rolling Mill
Load shock dominant & variable pass schedules.
Motor in Paper Dryer
Moisture ingress & felt tension cycles.
PlantOS™ Result
Each asset gets a model trained on its exact operating context — not generic pattern-matching.
✓ No Cold-Start
✓ >90% Day-1 Accuracy
✓ Zero False Alarm Fatigue

Why does the same motor need a different model
in a kiln, a mill, and a paper dryer?