Technology for IndustriesWhere Digital
Meets Physical.

Invences focuses on environments where connectivity, compute, AI, machines, and mission-critical operations converge.

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01 / Telecommunications

Intelligent & Autonomous Networks

Today
4G/5G • RAN • Core • IMS • Cloud • Edge • Integration • Operations
Evolving
AI-RAN • NTN • Network Digital Twins • Agentic Operations • 6G
Direction
AI-native and autonomous networks.

Industry use cases

Autonomous Network Operations

Turn fragmented network alarms into coordinated investigation, approved recovery and verified service restoration.

Powered by
  • iFabric™
  • Agentic AI
  • nLLM™
  • Digital Twin
  • Open RAN
  • 5G Core
Business impact targets
  • NOC OpEx target: decrease
  • MTTR target: decrease
  • Truck Rolls target: decrease
  • Availability target: increase
How it works

iFabric™ connects RAN, Core, transport, cloud and edge topology with service telemetry. Network intelligence and specialist agents investigate the cause and evaluate corrective actions against a Digital Twin. Approved remediation runs within defined permissions, followed by service-recovery checks.

AI-RAN & Distributed Compute

Use shared radio and edge infrastructure to support mobile services alongside enterprise AI inference.

Powered by
  • AI-RAN
  • GPU Infrastructure
  • Edge AI
  • 5G Advanced
  • Agentic Orchestration
Business impact targets
  • Infrastructure Utilization target: increase
  • Energy Efficiency target: increase
  • AI Revenue Potential target: increase
  • Cost per Inference target: decrease
How it works

GPU-enabled RAN and edge capacity supports radio workloads and eligible AI applications. Orchestration places inference on available resources while protecting network service requirements, latency budgets and compute limits.

Network Capacity Digital Twin

Test growth, coverage and service-launch plans before committing capacity or changing the live network.

Powered by
  • iFabric™
  • Digital Twin
  • Agentic AI
  • RF & Traffic Analytics
  • Cross-Domain Telemetry
Business impact targets
  • CapEx Efficiency target: increase
  • Planning Time target: decrease
  • Change Risk target: decrease
  • Network Utilization target: increase
How it works

A network Digital Twin combines topology, radio coverage, traffic, subscriber demand and infrastructure capacity. AI agents compare growth, outage and configuration scenarios so engineers can choose a capacity plan and review changes before production.

Explore the operating approach

Sites and connectivity create an opportunity. Delivering an AI service also takes a shared view of capacity, workload policies and operations across vendors and regions.

Capacity without context

Teams need to know which sites can support a workload, under which operating conditions.

Signals in silos

Network, compute and application events need to be connected to the service they affect.

Economics to establish

Workload placement must account for demand, capacity, policy and the cost of delivery.

What we bring together

  1. Qualify the estate

    Assess candidate sites, connectivity and compute against a defined regional service.

  2. Activate the AI Grid

    Integrate operational context through iFabric and workload placement policies through gFabric.

  3. Make it repeatable

    Validate service requirements, establish monitoring and build a model for onboarding further sites.

From AI factory to the field

  1. AI Factory

    Central compute

  2. AI Grid

    Regional AI services

  3. Edge

    Enterprise hubs

  4. Physical AI

    Connected operations

The operating goal

A repeatable way to qualify sites, place workloads and operate regional AI services with clear service and cost visibility.

Measures to define together

  • Site qualification time
  • Workload onboarding time
  • Cost per defined workload
Industry context & reading

02 / Manufacturing

Connected & Autonomous Factories

Today
Private 5G • Edge • IoT • Cloud • Data Center • Security
Evolving
Edge AI • Vision AI • Digital Twins • Connected Machines
Direction
Robotics, Physical AI and autonomous manufacturing.

Industry use cases

Autonomous Production & Material Flow

Coordinate production equipment, robots and material movement as one connected factory operation.

Powered by
  • Private 5G
  • Edge AI
  • Agentic AI
  • Physical AI
  • Digital Twin
Business impact targets
  • OEE target: increase
  • Work in Progress target: decrease
  • Labor Efficiency target: increase
  • Throughput target: increase
How it works

Private 5G and edge systems connect machines, mobile robots, vision systems and production data. Agents coordinate production sequences and material movement, while robot controllers execute approved tasks within factory safety constraints.

Coordinated Maintenance Operations

Turn an early equipment warning into a maintenance plan that accounts for parts, people and production.

Powered by
  • Private 5G
  • Industrial IoT
  • Edge AI
  • Agentic AI
  • Digital Twin
Business impact targets
  • Unplanned Downtime target: decrease
  • Maintenance OpEx target: decrease
  • Spare Inventory target: decrease
  • Asset Life target: increase
How it works

Edge AI analyzes vibration, thermal, electrical and machine telemetry for signs of deterioration. An agent checks production impact, parts availability and maintenance windows, then prepares a work order and revised schedule for approval.

ISAC Factory Safety

Emerging

Explore wireless sensing that helps robots and people share a busy factory floor more safely.

Powered by
  • ISAC
  • Private 5G
  • Computer Vision
  • Digital Twin
  • Physical AI
Business impact targets
  • Safety Incidents target: decrease
  • Sensor Duplication target: decrease
  • Automation Density target: increase
  • Productivity target: increase
How it works

Evaluate emerging RF sensing alongside cameras, positioning and machine telemetry in a spatial Digital Twin. Test how detected conflicts can inform approved robot routes and speed zones while established safety systems retain control.

ISAC and 6G use here is an evaluation direction; sensing capability and safety integration require validation.

Explore the operating approach

Machine vision, connected equipment and changing production layouts place different demands on the network and local compute. Teams need a clear infrastructure boundary before expanding a pilot or changing a line.

Different workload demands

Cameras and connected equipment need connectivity and compute sized for their actual traffic and processing requirements.

Limited fault context

A delayed application needs investigation across network, compute and equipment dependencies.

Production changes

Line moves and new workloads need acceptance checks and a reviewed recovery plan.

What we bring together

  1. Define the line boundary

    Agree the process requirement, equipment interfaces and intended operating result.

  2. Engineer the supporting infrastructure

    Integrate connectivity, local compute and operational signals for the selected workload.

  3. Validate the change

    Use lab checks before deployment, then review integration and acceptance against agreed criteria.

From AI factory to the field

  1. AI Factory

    Model development

  2. AI Grid

    Multi-site capacity

  3. Edge

    Factory inference

  4. Physical AI

    Line-side applications

The operating goal

A defined path from a production requirement to integrated infrastructure, validation checks and accountable operations.

Measures to define together

  • Infrastructure-related interruptions
  • Time to isolate faults
  • Change success rate
Industry context & reading

03 / Smart Agriculture

Intelligence from Network to Field

Today
Private 5G • IoT • Sensors • Edge • Precision Agriculture
Evolving
Edge AI • Vision • Digital Twins • ISAC
Direction
Agricultural robotics, autonomous equipment and intelligent farming.

Industry use cases

Autonomous Precision Farming

Target irrigation, crop treatment and machinery work to the parts of the farm that need action.

Powered by
  • Private 5G
  • Edge AI
  • Agentic AI
  • Drones
  • Autonomous Machinery
  • Physical AI
Business impact targets
  • Water Use target: decrease
  • Chemical Use target: decrease
  • Labor Demand target: decrease
  • Yield target: increase
How it works

Private 5G connects field sensors, cameras, drones and farm equipment, while edge AI identifies crop stress, weeds and irrigation needs. Farm agents coordinate field tasks, with approved irrigation, spraying and machinery actions executed through connected control systems.

Farm Digital Twin

Compare irrigation, fertilization and harvest decisions before committing water, inputs or equipment.

Powered by
  • Digital Twin
  • Edge AI
  • Agentic AI
  • IoT
  • Private 5G
Business impact targets
  • Input Costs target: decrease
  • Crop Loss target: decrease
  • Forecast Accuracy target: increase
  • Yield per Acre target: increase
How it works

A farm Digital Twin brings together soil conditions, crop imagery, weather, irrigation and machinery data. Agents simulate operating choices and present resource needs and crop risks for the farm team to review.

Satellite-Backed Rural Private 5G

Connect remote farms to local applications and machinery when terrestrial backhaul is limited.

Powered by
  • Satellite / NTN
  • Private 5G
  • Local 5G Core
  • Edge AI
  • Autonomous Equipment
Business impact targets
  • Coverage target: increase
  • WAN Dependency target: decrease
  • Deployment Time target: decrease
  • Rural Automation target: increase
How it works

Satellite backhaul links a private 5G network with a local Core and edge platform. Locally supported farm applications and equipment can continue within available site resources during WAN interruptions, with synchronization when connectivity returns.

Explore the operating approach

Field sensors and local processing need a practical plan for coverage, power, backhaul and maintenance. Start with the operating conditions and one defined workload before extending the system.

Uneven connectivity

Field locations and available backhaul shape the options for carrying sensor data.

Site constraints

Power, equipment placement and access for maintenance need to be established in a survey.

Workload boundaries

Sampling, data transfer and local processing need requirements before equipment is selected.

What we bring together

  1. Define the field requirement

    Agree sensor locations, data needs and operating constraints with the farm team.

  2. Design the infrastructure

    Assess connectivity and local compute options for the selected application and site.

  3. Validate against the scope

    Plan lab and field acceptance checks, with named operational responsibilities.

From AI factory to the field

  1. AI Factory

    Application requirements

  2. AI Grid

    Available backhaul

  3. Edge

    Local processing

  4. Physical AI

    Field observations

The operating goal

An agreed engineering scope for field connectivity or local processing, with test criteria and a clear path to operational handover.

Measures to define together

  • Coverage at agreed sensor locations
  • Data-delivery acceptance results
  • Time to restore a field connection
Industry context & reading

    04 / Logistics & Transportation Hubs

    Connecting Autonomous Movement

    Today
    Private Networks • IoT • Edge • Tracking • Video
    Evolving
    Computer Vision • Digital Twins • Edge AI
    Direction
    Robotics, AGVs, autonomous vehicles, intelligent warehouses, ports and transportation hubs.

    Industry use cases

    Autonomous Yard & Terminal Operations

    Coordinate vehicles, robots, docks and gates around changing demand at a busy logistics hub.

    Powered by
    • Private 5G
    • Edge AI
    • Agentic AI
    • Physical AI
    • Digital Twin
    Business impact targets
    • Dwell Time target: decrease
    • Labor Cost target: decrease
    • Asset Utilization target: increase
    • Throughput target: increase
    How it works

    Private 5G connects terminal equipment, mobile robots, cameras and workforce systems. Agents allocate docks, vehicles and charging slots from live demand, then dispatch approved tasks through fleet and equipment controllers.

    Intelligent Cargo & Asset Flow

    Keep cargo moving when an arriving truck, aircraft, train or shipment changes the plan.

    Powered by
    • Private 5G
    • IoT
    • Computer Vision
    • Digital Twin
    • Agentic AI
    Business impact targets
    • Turnaround Time target: decrease
    • Lost Assets target: decrease
    • SLA Performance target: increase
    • Inventory Efficiency target: increase
    How it works

    Location, video, IoT and shipment data feed a live Digital Twin of the hub. When a delay occurs, agents recalculate dock allocation, staffing and downstream movements, then coordinate approved updates with warehouse and fleet systems.

    ISAC Hub Traffic Orchestration

    Emerging

    Explore RF sensing to anticipate conflicts between vehicles, equipment and people across a transport hub.

    Powered by
    • 5G Advanced / 6G
    • ISAC
    • Digital Twin
    • Agentic AI
    Business impact targets
    • Collisions target: decrease
    • Congestion target: decrease
    • Sensor Duplication target: decrease
    • Hub Capacity target: increase
    How it works

    Evaluate ISAC observations alongside cameras and positioning data in an operational Digital Twin. Test conflict detection and route or zone recommendations before enabling approved changes through facility safety and traffic systems.

    RF sensing and automated traffic responses require site trials, safety validation and approved operating limits.

    Explore the operating approach

    Mobile fleets cross coverage zones and depend on application services beyond a single access point. A connectivity problem needs to be traced across the route, network and supporting edge infrastructure.

    Coverage along a route

    Movement through aisles, loading areas and handover zones changes the operating conditions.

    Shared dependencies

    A fleet application can be affected by radio, transport, edge compute or application issues.

    Handover to operations

    Support teams need agreed service checks and a way to escalate an infrastructure fault.

    What we bring together

    1. Survey the operating route

      Define the fleet application, movement areas and connectivity requirements.

    2. Integrate the service path

      Engineer the selected connectivity and supporting infrastructure around the workload boundary.

    3. Validate and support

      Check coverage and handovers in the agreed environment, then establish monitoring and fault ownership.

    From AI factory to the field

    1. AI Factory

      Application preparation

    2. AI Grid

      Depot capacity

    3. Edge

      Local services

    4. Physical AI

      Mobile fleets

    The operating goal

    A defined connectivity and operating plan for a mobile-fleet workload, with coverage checks and clear fault ownership.

    Measures to define together

    • Route coverage against agreed criteria
    • Time to isolate connectivity faults
    • Handover acceptance results
    Industry context & reading

      05 / Energy & Utilities

      Bring intelligence closer to the asset.

      Connect remote signals and local reasoning to a reviewed operational response.

      Power networks · Oil & gas · Remote infrastructure

      • Assets
      • Edge
      • Response

      Industry use cases

      Agentic Grid & Field Operations

      Coordinate equipment events, storage decisions and field response across distributed energy assets.

      Powered by
      • Private 5G
      • Edge AI
      • Agentic AI
      • Digital Twin
      • iFabric™
      Business impact targets
      • Outage Duration target: decrease
      • Truck Rolls target: decrease
      • Grid Utilization target: increase
      • Operations OpEx target: decrease
      How it works

      Agents correlate grid telemetry, weather, equipment health, field activity and demand. They assess operating options and prepare approved storage, balancing or dispatch actions, with utility control systems and operators retaining authority over critical equipment.

      Autonomous Asset Inspection

      Find developing faults across remote energy assets and prioritize the right field intervention.

      Powered by
      • Private 5G
      • NTN
      • Drones
      • Robotics
      • Vision AI
      • Agentic AI
      Business impact targets
      • Inspection Cost target: decrease
      • Human Exposure target: decrease
      • Asset Availability target: increase
      • Failure Risk target: decrease
      How it works

      Connected drones and robots collect inspection imagery and sensor readings from lines, pipelines, renewable assets and substations. Vision AI flags anomalies, while agents rank their operational impact and prepare inspection findings and approved intervention tasks.

      Resilient Utility Private Network

      Keep essential crews, telemetry and site applications connected through public-network or WAN disruption.

      Powered by
      • Private 5G
      • iCore™
      • Edge
      • NTN
      • Network Slicing
      Business impact targets
      • Resilience target: increase
      • Outage Response Time target: decrease
      • Public-Network Dependency target: decrease
      • Operational Continuity target: increase
      How it works

      Private 5G, local Core and edge services support priority communications at the utility site. Satellite backup and tested failover paths reduce dependency on a single backhaul, with continuity defined by local capacity, power and operating requirements.

      Explore the operating approach

      Assets are distributed. Data and expertise are often separated. An inspection signal is useful only when the team can relate it to equipment condition, connectivity and the next approved action.

      Incomplete asset context

      Fragmented data and uneven digital infrastructure limit the usefulness of AI across an estate.

      Distance from expertise

      Remote facilities need local operational visibility and defined behavior when connectivity degrades.

      Consequential actions

      Operational automation needs boundaries, evidence and a clear route to human review.

      What we bring together

      1. Connect the relevant assets

        Integrate available equipment, network and edge signals into a shared operational picture.

      2. Reason near the site

        Use local infrastructure reasoning to support diagnosis, with workload placement defined by the environment.

      3. Govern the response

        Define action permissions, escalation and recovery procedures before expanding automation.

      From AI factory to the field

      1. AI Factory

        Model preparation

      2. AI Grid

        Regional operations

      3. Edge

        Remote-site reasoning

      4. Physical AI

        Assets & inspection

      The operating goal

      Remote operations with connected asset context, local reasoning and a traceable path from an observed issue to an approved response.

      Measures to define together

      • Time to diagnose a site issue
      • Asset observability coverage
      • Time from alert to reviewed action
      Industry context & reading

      06 / Smart Cities & Public Infrastructure

      Connect the city behind the service.

      Bring infrastructure signals, service ownership and approved responses into one operating view.

      Municipal operators · Transport estates · Public facilities

      • Assets
      • Ownership
      • Response

      Industry use cases

      City Operations Agent

      Bring municipal incidents and service disruptions into one coordinated response workflow.

      Powered by
      • 5G
      • Edge AI
      • Digital Twin
      • Agentic AI
      • Computer Vision
      Business impact targets
      • Response Time target: decrease
      • Congestion target: decrease
      • Operator Workload target: decrease
      • Service Levels target: increase
      How it works

      Traffic, transit, IoT, weather and infrastructure signals feed a city Digital Twin. Agents assess impact and coordinate response tasks, with approved signaling or service-route changes applied through the responsible municipal systems.

      Autonomous Infrastructure Inspection

      Turn imagery from public assets into a prioritized maintenance queue for municipal teams.

      Powered by
      • Private / Public 5G
      • Edge AI
      • Drones
      • Physical AI
      • Agentic AI
      Business impact targets
      • Inspection OpEx target: decrease
      • Maintenance Backlog target: decrease
      • Asset Life target: increase
      • Public Safety target: increase
      How it works

      Drones, robotic systems and municipal vehicles gather imagery and sensor readings from roads, bridges and utilities. Edge AI flags defects for review, and agents assemble location, severity and evidence into repair tasks for the responsible team.

      ISAC Intelligent Mobility

      Emerging

      Explore wireless sensing that helps cities understand movement and improve traffic response.

      Powered by
      • 5G Advanced / 6G
      • ISAC
      • AI-RAN
      • Edge AI
      • Digital Twin
      Business impact targets
      • Congestion target: decrease
      • Accidents target: decrease
      • Travel Time target: decrease
      • Sensor Infrastructure target: decrease
      How it works

      Evaluate RF sensing alongside roadside cameras and sensors to observe vehicles, pedestrians and drones. A Digital Twin supports traffic and emergency-route scenarios, with approved interventions kept within municipal traffic-control rules.

      ISAC and AI-RAN sensing integration is an evaluation direction requiring local trials and validated traffic-control interfaces.

      Explore the operating approach

      Sensors and individual projects do not create a coordinated operating model. Teams need shared context across vendors and departments, alongside clear responsibility for how data and automation are used.

      Projects in isolation

      Different systems and operators make it difficult to connect infrastructure events across services.

      Unclear handoffs

      An alert needs an owner, an operating context and an agreed response process.

      Trust in deployment

      Data use, access and automated actions need explicit governance as connected services expand.

      What we bring together

      1. Choose a service boundary

        Start with a defined estate, such as public facilities or an infrastructure inspection program.

      2. Join the operating context

        Connect supported asset and service telemetry, with edge processing where the use case requires it.

      3. Make responsibility visible

        Define ownership, action policies and service measures before extending the operating model.

      From AI factory to the field

      1. AI Factory

        Shared AI capability

      2. AI Grid

        Regional services

      3. Edge

        Local processing

      4. Physical AI

        Facilities & field assets

      The operating goal

      A coordinated operating view of a defined public infrastructure estate, with accountable ownership from an observed fault to its resolution.

      Measures to define together

      • Time to assign an incident owner
      • Connected asset coverage
      • Service incident resolution time
      Industry context & reading

      07 / AI Infrastructure

      Put AI capacity to work.

      Connect compute, power, cooling and workload context across the AI factory.

      Data centers · AI cloud providers · Sovereign compute

      • Compute
      • Capacity
      • Readiness

      Industry use cases

      Autonomous AI Factory Operations

      Coordinate GPU, network, storage and facility operations around the workloads they support.

      Powered by
      • iFabric™
      • Agentic AI
      • Digital Twin
      • GPU Infrastructure
      • Network & Facility Telemetry
      Business impact targets
      • GPU Utilization target: increase
      • Energy per GPU target: decrease
      • Downtime target: decrease
      • Infrastructure OpEx target: decrease
      How it works

      iFabric™ discovers infrastructure relationships and correlates compute, network, power and cooling telemetry in an operational Digital Twin. Agents investigate bottlenecks, anticipate service risks and coordinate approved remediation across the responsible systems.

      Intelligent GPU & Workload Orchestration

      Place AI workloads on capacity that balances cost, performance and service requirements.

      Powered by
      • Agentic AI
      • GPU Infrastructure
      • High-Speed Networking
      • Edge / Cloud
      • iFabric™
      Business impact targets
      • Cost per Inference target: decrease
      • GPU Utilization target: increase
      • Energy Efficiency target: increase
      • SLA Performance target: increase
      How it works

      Agents evaluate GPU availability, network congestion, power cost, thermal headroom and workload priority. Placement decisions respect latency, data-location and access policies, then coordinate approved scheduling changes and check the resulting service performance.

      Distributed AI Grid

      Route inference across central, regional and edge capacity according to each application’s requirements.

      Powered by
      • AI-RAN
      • Edge AI
      • Data Centers
      • Agentic Orchestration
      • High-Speed Networking
      Business impact targets
      • Inference Latency target: decrease
      • Infrastructure Utilization target: increase
      • Data Transport Cost target: decrease
      • AI Reach target: increase
      How it works

      Applications provide latency, data-location and capacity requirements to an orchestration layer. It selects eligible AI factory, regional or enterprise-edge resources; validated AI-RAN capacity can join the grid as that integration develops.

      Explore the operating approach

      Installed GPUs are only part of the system. Power, cooling, networks and storage have to support the workload together, while operators need to see where capacity is constrained.

      Physical constraints

      Power density and changing load profiles complicate the move from conventional compute to AI infrastructure.

      Fragmented visibility

      A component can appear healthy while a workload waits on a network, storage or thermal bottleneck.

      Operational readiness

      Commissioning infrastructure and handing it over to a repeatable operating model are different jobs.

      What we bring together

      1. Establish the baseline

        Assess the existing estate, workload requirements and dependencies before defining the architecture.

      2. Connect the operating picture

        Bring discovery, observability and infrastructure context together through iFabric.

      3. Operate against demand

        Use governed workload placement and operational workflows to manage capacity and service health.

      From AI factory to the field

      1. AI Factory

        Training & inference

      2. AI Grid

        Regional capacity

      3. Edge

        Local inference

      4. Physical AI

        Field applications

      The operating goal

      An AI factory whose capacity, dependencies and service health can be understood and operated as one system.

      Measures to define together

      • Workload readiness time
      • Usable compute utilization
      • Time to isolate bottlenecks
      Industry context & reading

      08 / Healthcare

      Bring AI inside approved boundaries.

      Map dependencies, govern workload placement and review changes across healthcare infrastructure.

      Hospitals · Health systems · Healthcare campuses

      • Visibility
      • Boundaries
      • Continuity

      Industry use cases

      Intelligent Connected Hospital

      Connect mobile equipment, clinical applications and hospital AI through one policy-aware infrastructure.

      Powered by
      • Private 5G
      • Edge AI
      • Network Slicing
      • IoT
      • Agentic Operations
      Business impact targets
      • Equipment Search Time target: decrease
      • Workflow Delays target: decrease
      • Connectivity Reliability target: increase
      • Staff Productivity target: increase
      How it works

      Private 5G links devices, imaging services, staff communications and asset-tracking systems with local edge applications. Orchestration prioritizes connectivity and compute around agreed clinical and operational needs, with clinical decisions remaining with healthcare professionals.

      Physical AI Hospital Logistics

      Move routine supplies and samples through the hospital while staff focus on patient-facing work.

      Powered by
      • Private 5G
      • Edge AI
      • AMRs
      • Physical AI
      • Digital Twin
      Business impact targets
      • Non-Clinical Workload target: decrease
      • Delivery Time target: decrease
      • Staff Productivity target: increase
      • Automation Coverage target: increase
      How it works

      Autonomous mobile robots carry approved loads across mapped hospital routes. Private 5G and edge intelligence coordinate fleet movement, elevators and restricted areas through approved interfaces, with staff able to supervise and intervene.

      Contactless Patient & Facility Sensing

      Emerging

      Explore local RF sensing to support presence, movement and fall alerts without relying solely on cameras.

      Powered by
      • ISAC
      • Private 5G / 6G
      • Edge AI
      • IoT
      Business impact targets
      • Monitoring Coverage target: increase
      • Manual Observation Burden target: decrease
      • Local Data Processing target: increase
      • Response Time target: decrease
      How it works

      Evaluate emerging RF sensing alongside approved IoT and clinical systems, with sensitive data processed locally under defined access and retention policies. Validated alerts support staff observation and response; clinical decisions remain with healthcare professionals.

      RF sensing for patient-related alerts requires clinical validation and applicable approvals before use in care.

      Explore the operating approach

      New AI workloads arrive in an environment of existing systems, sensitive information and essential services. Teams need to know what is connected, where workloads run and how a change affects operations.

      Unknown dependencies

      Unmanaged assets and incomplete inventories make infrastructure risk harder to understand.

      Sensitive workloads

      Placement, access and data movement need to follow the organization’s requirements for each use case.

      Continuity through change

      Operational teams need visibility and review procedures as new services enter the estate.

      What we bring together

      1. Understand the estate

        Map supported infrastructure assets and dependencies, and connect operational telemetry through iFabric.

      2. Define workload boundaries

        Set placement and access policies through gFabric for the selected AI environment.

      3. Introduce controlled operations

        Validate changes, establish monitoring and define human review for consequential infrastructure actions.

      From AI factory to the field

      1. AI Factory

        Approved model preparation

      2. AI Grid

        Approved shared capacity

      3. Edge

        Campus AI workloads

      4. Physical AI

        Connected infrastructure

      The operating goal

      A governed infrastructure foundation for local AI workloads, with visible dependencies, defined data handling and accountable operational changes.

      Measures to define together

      • Infrastructure inventory coverage
      • Workload policy adherence
      • Service restoration time
      Industry context & reading

      Start with your environment.

      Talk to our team