The Prescriptive AI Platform
Behind Every Outcome.
Platform Walkthrough
See PlantOS™ in Action
T5
Decision & Planning
VALUEThe strategic layer — turning condition and process intelligence into defensible, risk-aligned plant decisions, with Dynamic FMEA re-ranking every failure mode against live equipment behaviour.
Plant Fusion – Maintenance
Decision authority that continuously prescribes a dynamic, risk-first PM program.
Prescriptive
Maintenance
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
- 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
- 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
VALUEThe 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.
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
- 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
INFRAThe 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.
Process Canvas
Operational intelligence platform converting plant historian data into structured live KPIs.
Process
&
Energy Efficiency
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
- 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
- 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
- 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
- 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
INFRAThe connective tissue — secure, multi-protocol transmission from plant floor to PlantOSTM via a universal edge gateway, with edge inferencing and RPM-triggered edge spectroscopy processing data at the source.
EdgeIU
Process Edge Intelligence Unit capturing temperature, pressure, and energy data via tag reading.
Process
&
Energy Efficiency
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
INFRAThe foundation layer — complete condition data capture from every critical rotating asset, built on the market's broadest sensing portfolio: wired, wireless, piezo, MEMS, self-powered, and third-party sensors.
Critical Sensor Kit
Five purpose-built sensors providing complete critical rotating asset coverage.
Prescriptive
Maintenance
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
- 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
