Prediction ≠ Outcomes
Leaders govern outcomes — not reports. Industrial Agentic AI, delivered by PlantOS™.
Thank you for your interest in
PlantOS™ Prescriptive AI.
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3 outcomes in 1 prescription — zero guesswork.
Prescriptions
Generated till date*
up to
Prescriptions
Acted Upon
up to
Reduced Cost of
maintenance/unit
up to
Reduced Cost
of Energy/unit
up to
Increased
Utilization Rate
Plants
Digitalized Globally
Unplanned
Downtime Hours
Eliminated*
Industrial operations have never had more data.
Outcomes remain
elusive.
The market has delivered capability in layers — sensors, dashboards, integrated
monitoring, advanced
analytics. Each a step forward. None of them enough.
Because all of it stops at the alert. No governance. No validated action. No closed loop.
Real-time machine signals captured continuously.
Visual asset health and performance KPIs.
Unified cross-system monitoring and context.
AI prescribes actions. Operators validate outcomes.
The Outcome Gap
The gap between what AI recommends and
what actually gets done - that's the Outcome Gap.
The reason 95% of industrial AI pilots never scale.
A Prescription Engine.
With Guaranteed Outcomes.
Zero Guesswork.
And it's built that way - deliberately, architecturally, layer by layer.
Six Layers. One
99% Trust Loop.
PlantOS™ is organised across six technology tiers. T1–T3 form the Infrastructure Layer — data and execution foundation. T4–T6 form the Value Layer — where AI, prescriptions, and governed decisions generate measurable ROI. Data flows upward. Intelligence flows back down. T6 loops back into T3, closing the prescription-to-execution loop across the fleet.
Strategic Governance COO · CFO · CDO
Scheduling Agent
Decision & Planning
Analytics & Prescription
Platform & Execution
Edge & Connectivity
Sense & Ingest
The 99% Trust Loop
A continuous feedback loop that runs every prescription into an operator-validated outcome.
One Platform. Six Layers.
15 Products. Live
Today.
Explore the architecture. Every layer, every product, built to close the loop.
Click any
product to
see
exactly how it delivers outcomes.
Strategic Governance
VALUEThe executive layer — tracking prescription accountability, quantifying business impact, and benchmarking across the fleet.
Prescription Engine — Multi-Site ROI Governance
The governance layer tracking prescription completion and quantifying business impact across the fleet.
- The governance layer receiving prescriptions from Plant Fusion and Process Prescript
- Tracks whether corrective actions were completed and quantifies business impact
- Enables fleet-level benchmarking across multiple plants
- Answers the executive question: "Are our AI recommendations being acted on, and what is the business value?"
- 100% prescription accountability — every recommendation tracked to outcome
- Multi-site benchmarking across SHC, PM completion rate, and MTTR
- Plant-by-plant ROI attribution across 12 KPIs — board-ready reports auto-generated
- Closes the loop back to T3 — validated outcomes retrain AI prescriptions across the fleet
- Prescriptions acknowledged but not executed deliver zero ROI
- Executives need accountability — not dashboards of unacted alerts
- Multi-plant operators need apples-to-apples benchmarking across sites
- Board-level reporting requires verified, attributable ROI per plant
Scheduling Agent
AGENTThe orchestrating intelligence — an autonomous multi-agent system synthesising risk, resource, production, and spares constraints.
Multi-Agent Constraint Optimizer
Multi-agent AI system optimising maintenance schedules across competing constraints.
- A multi-agent AI system automatically optimizing maintenance schedules
- Synthesises risk, resource, production, and spares insights for most optimal outcomes
- Autonomous orchestrator sitting between Plant Fusion decisions and AI Worx execution
- Launching June 2026
- 7 inputs synthesised — risk, resource, production, spares, lead time, criticality, cost
- 8–12 day average RUL extension via intelligent speed reduction
- 3–4 unplanned stoppages avoided per plant per year
- Human-gated governance — agent cannot override safety or production priorities
- Human schedulers cannot optimally balance 7+ competing constraints in real time
- Production schedules, spare availability, technician capacity, and asset risk shift continuously
- Static maintenance schedules create either production conflict or excess downtime risk
- Multi-plant operators need automated scheduling that respects governance rules
Decision & Planning
VALUEThe strategic layer — turning condition and process intelligence into defensible, risk-aligned plant decisions.
Plant Fusion – Maintenance
Decision authority that continuously prescribes a dynamic, risk-first PM program.
- 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
- 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
- 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
Plant Fusion – Spares
Governed engine prescribing risk-aligned min/max levels and ordering triggers.
- 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
- 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
- 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
Plant Fusion – Decision
Strategic decision layer synthesising health, process, energy, and spend into one plant view.
- The strategic decision layer synthesising equipment health, process, energy, and maintenance spend
- One plant view designed for plant directors and COOs
- Unifies operational intelligence across PlantOS™ into a single executive lens
- Links maintenance decisions to production, throughput, energy, and OEE
- Plant-level OPEX vs. operational risk visibility — single view linking maintenance to production
- CAPEX decisions backed by AI failure probability forecasts and asset remaining life estimates
- Connects maintenance decisions to production throughput, energy cost, and OEE
- The plant director's command view — not just another maintenance KPI dashboard
- Individual asset tools can't answer plant director questions on cost, concentration, or replacement economics
- Plant leadership needs one view connecting maintenance to business outcomes
- CAPEX decisions require failure probability forecasts and asset remaining life estimates
- Maintenance KPIs alone don't tell the production story
Analytics & Prescription
VALUEThe AI brain — converting condition and process data into specific, evidence-backed prescriptions.
Equipment Prescript
AI engine converting vibration and condition signatures into structured mechanical fault prescriptions.
- The AI Prescription engine converting vibration and condition signatures into structured mechanical fault prescriptions
- Combines spectrum analysis, envelope detection, and bearing/gear mesh analysis
- Outputs plain-language, machine-readable prescriptions with RUL and urgency class
- Feeds directly into Plant Fusion — Maintenance and AI Worx
- 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 unbalance, misalignment, bearing defects, gear defects, looseness, pump flow issues
- Feeds directly into Plant Fusion — Maintenance as condition input for PM strategy
- Raw vibration spectra require expert interpretation — most plants can't scale specialists across every asset
- Manual fault diagnosis is slow, inconsistent, and misses subtle signatures
- Without structured prescriptions, AI detections don't convert into work orders
- RUL estimates are essential for run/repair/replace decisions
Process Prescript
AI prescription layer on Process Canvas — turning visibility into operator guidance.
- The AI prescription layer on top of Process Canvas — transforming visibility into operator guidance
- Four domain modules: Energy, Throughput, Yield, and Quality
- Issues specific, evidence-backed corrective actions like a clinical prescription
- Quantified expected impact per prescription with historical evidence
- 3–8% energy cost reduction — prescriptions delivered before energy is wasted
- 1–3% throughput improvement by eliminating bottlenecks before they constrain production
- Multi-variable correlation prevents single-sensor false positives and alert fatigue
- Every prescription includes deviation, action, expected impact, and historical evidence
- Process anomalies cause energy waste, yield loss, and quality failures before operators detect them
- Multi-variable correlations invisible in single-sensor dashboards
- Operators need evidence, not alerts — specific action with expected impact
- Single-sensor alerts cause false positives and alert fatigue
Platform & Execution
INFRAThe operational layer — where raw data becomes structured intelligence and AI prescriptions become executed work.
Process Canvas
Operational intelligence platform converting plant historian data into structured live KPIs.
- 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
- 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
- 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
AI Worx
Maintenance execution layer closing the loop between AI prescriptions and real work.
- 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
- 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
- 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
DigiLogBook
Digital replacement for paper shift logs — structured handover and field observation capture.
- 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
- 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
- Paper logbooks trap observations in unsearchable silos
- Critical operator context lost at retirement — institutional memory evaporates
- AI fault prescriptions without human context produce false positives and alert fatigue
- Shift handover gaps cause avoidable incidents and rework
AI Integration
Bidirectional integration layer connecting PlantOS™ with SAP, Oracle, Maximo, MES, LIMS.
- Bidirectional integration layer connecting PlantOS™ with SAP PM, Oracle EAM, IBM Maximo, MES, LIMS, and custom ERP
- API-first architecture with full Swagger/OpenAPI documentation
- MCP Protocol support for enterprise IT teams
- The choice for plants with strong existing CMMS infrastructure
- ERP purchase orders triggered automatically by AI-detected RUL thresholds
- MES production orders enrich Process Canvas KPIs with live context
- Zero parallel data entry — AI Worx and SAP PM stay synchronized automatically
- MCP Protocol support for modern AI-native integration
- Enterprises have massive investments in SAP, Oracle, Maximo — AI must coexist, not replace
- Parallel data entry across AI and CMMS creates errors and slows execution
- Quality events in LIMS must close the loop back into Process Prescript
- Production context from MES must enrich process KPIs in real time
SenseLink
Connector integrating existing third-party sensor ecosystems into PlantOS™.
- The connector integrating existing third-party sensor ecosystems into PlantOS™
- Supports competitor platforms — Augury, Tractian, and others
- Authenticates via customer's existing sensor credentials or APIs
- Feeds condition signatures into PlantOS™ Equipment Prescript for AI-driven fault prescription
- 100% of existing sensor investments preserved — no capital write-off to adopt PlantOS™
- Unified engine — all condition data feeds a single Equipment Prescript
- Governed — SenseLink data feeds Plant Fusion for risk-aligned PM decisions
- Plants adopt PlantOS™ value layer without displacing incumbent sensor infrastructure
- Plants have already invested in third-party sensor platforms — capital write-off blocks AI adoption
- 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
- Without SenseLink, plants face an either/or choice between incumbent sensors and AI value
Edge & Connectivity
INFRAThe connective tissue — secure, multi-protocol data transmission from plant floor to PlantOS™.
EdgeIU
Process Edge Intelligence Unit capturing temperature, pressure, and energy data via tag reading.
- 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
- 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
- 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
Sense & Ingest
INFRAThe foundation layer — complete condition data capture from every critical rotating asset.
Critical Sensor Kit
Five purpose-built sensors providing complete critical rotating asset coverage.
- 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
- 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
- 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
IDRS Managed Diagnostics
Managed reliability service combining offline CBM, online oil monitoring, and RCFA.
- 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
- 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
- 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
INDUSTRIES
Proven Across Heavy Industries
2026: The Trust Architecture
of
Industrial AI.
Prediction ≠ Outcomes. Governance is the missing layer.
See how PlantOS™ closes the Outcome Gap – prescription, validation, and governance in one loop.
Thank you for your interest in
PlantOS™ Prescriptive AI.
Enter your official work email to unlock specs delivering
3 outcomes in 1 prescription — zero guesswork.