AI for Smart Infrastructure

Our point of view

Unified digital twins that optimise comfort, energy and asset life together.

We start where the operating constraint is, connect the systems that hold the truth, and only then choose the model. Governance is designed with the workflow, not bolted on.

Priority AI opportunities

Digital twins and scenario simulation
Energy-consumption optimisation
Predictive asset maintenance

Use cases

Smart Infrastructure use cases in production scope.

Business function

AI technology

Business outcome

Implementation horizon

3 use cases

Predictive MLOperations3-6 months

Predictive asset maintenance

Critical equipment fails without warning, forcing unplanned production stops.

AI approach: Multivariate anomaly and remaining-useful-life models per asset class, deployed at the edge.
Required data: Historian tags, vibration and thermal sensors, maintenance logs.
Systems involved: SCADA, CMMS, ERP
Expected impact: Cost — Fewer unplanned stoppages; maintenance shifted into planned windows.
Time to value: 3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

OptimisationSustainability3-6 months

Energy-consumption optimisation

Energy is managed by static setpoints, not live conditions.

AI approach: Forecast-driven optimisation with operator-approved setpoint changes.
Required data: Meters, weather, production plans, tariffs.
Systems involved: BMS, SCADA, ERP
Expected impact: Sustainability — Reduced energy intensity at constant output.
Time to value: 3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

OptimisationEngineering & R&D6-12 months

Digital twins and scenario simulation

Changes are tested in production because there is no safe sandbox.

AI approach: Twin runtime fed by live telemetry, used for what-if simulation.
Required data: Asset models, telemetry, process parameters.
Systems involved: MES, historian, PLM
Expected impact: Quality — Fewer disruptive trials and better change decisions.
Time to value: 6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Reference architecture

How the intelligence connects.

BMS / IoT→Spatial model→Twin runtime→Optimisation agents

Business impact indicators

Energy use-13% Illustrative

Reactive callouts-27% Illustrative

Asset life+8% Illustrative

Accelerators

Relevant accelerators.

Accelerator

Enterprise Agent Studio

Design, evaluate and operate governed agents against real enterprise tools.

Solves: Agent prototypes stall because permissions, testing and observability are missing.
Architecture: Tool registry, policy engine, evaluation harness, trace store.
Integrations: ERP, CRM, ITSM, Identity provider
Implementation: 6–10 weeks to first supervised agent in production.
Governance controls: Scoped permissions · Approval thresholds · Full traces

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Accelerator

AI Knowledge Hub

Entitlement-aware retrieval across every enterprise knowledge source.

Solves: Copilots answer without provenance or respect for access rights.
Architecture: Connectors, hybrid index, re-ranking, citation service.
Integrations: SharePoint, Confluence, Object storage, Ticketing
Implementation: 4–8 weeks to a governed knowledge surface.
Governance controls: Entitlement propagation · Citation enforcement · PII redaction

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Accelerator

Document Intelligence Engine

Turn high-volume documents into validated structured data.

Solves: Manual keying and review dominate document-heavy processes.
Architecture: Layout parsing, extraction models, confidence routing, review UI.
Integrations: ECM, Workflow, ERP
Implementation: 4–6 weeks per document family.
Governance controls: Confidence thresholds · Human review queues · Accuracy monitoring

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Accelerator