AI Native Technology

AI Infrastructure, Cloud & Platform Engineering

Cloud, GPU and platform foundations that run AI reliably at enterprise scale.

The foundation for AI at enterprise scale.

AI changes what infrastructure has to do. Inference runs continuously and its cost lands on every request; GPU capacity is scarce and expensive; data has to reach models with the latency and sovereignty the business demands. Foundations designed for yesterday's web applications are not designed for that.

Platform engineering here sizes compute, GPU and storage to the economics of inference in production, not only to the peak of training, and builds the serving, MLOps and observability foundations that let models and agents run reliably across hybrid and multi-cloud estates. Cost is an architectural constraint from the first design.

Fibre optic strands glowing red against a blue haze

Core capabilities

Engineering capabilities delivered end to end by a single accountable team

AI infrastructure

Compute, storage, networking and accelerators engineered for training and, above all, inference.

GPU infrastructure

GPU capacity planned, scheduled and shared so scarce accelerators are used well.

Cloud architecture

Landing zones, guardrails and services designed for intelligent workloads.

Hybrid and multi-cloud

Workloads placed by sovereignty, latency and cost across clouds and on premises.

AI platform engineering

Internal platforms that give teams governed, self-service access to models, data and tools.

Kubernetes and container platforms

Container platforms operated for scale, isolation and reliability.

Data platforms

Storage, lakehouse and streaming platforms that feed models at production speed.

Model serving

Serving infrastructure with routing, scaling and fallback for models in production.

MLOps infrastructure

Registries, pipelines and environments that make model operations repeatable.

AI FinOps

Cost visibility and control per model, per team and per request.

Platform observability

Metrics, logs and traces across platform and workloads in one picture.

High-performance computing

HPC capabilities for simulation, research and training at scale.

iFortis Worldwide®

Where it sits in the architecture

This domain engineers Cloud + GPU + Infrastructure within the enterprise architecture.

16:19  Mon 31 Aug
DoneWhere it sits in the architecture
CONTROL PLANESECURITY + GOVERNANCE + OBSERVABILITYIDENTITY · POLICY · MODEL RISK · AUDIT EVIDENCECONTROL PLANECLOUD + GPU + INFRASTRUCTURECOMPUTE · GPU · STORAGE · HYBRID AND MULTI-CLOUDINFRASTRUCTUREDATAPLATFORMS · LAKEHOUSEINTEGRATION + APISEVENT STREAMS · SYSTEMS OF RECORDDATA & INTEGRATIONAPPLICATIONSINTELLIGENT APPSMODELSEVALUATED · SERVEDAGENTSAGENTIC SDLCAPPLICATIONSRECORDSAGENTSPEOPLEPROCESSESPOLICYTIMEENTERPRISE INTELLIGENCE FABRIC + ONTOLOGYENTERPRISE GRAPH · ONTOLOGY · CONTEXT ENGINEINTELLIGENCE FABRICAI DIGITAL WORKFORCE + AGENTIC OPERATIONSDIGITAL WORKERS · AGENTS · AUTHORITY LIMITSDIGITAL WORKFORCEBUSINESS & OPERATING MODELDECISION RIGHTS · FUNDING · OUTCOMESBUSINESSAGENTRole definedAuthority limit setNamed human ownerGOVERNEDDECISIONPolicy checkedEvidence loggedReversibleTRACEABLEAI NATIVE TECHNOLOGY · ENTERPRISE ARCHITECTURE · SCALE 1:1
Business & Operating ModelAI Digital Workforce + Agentic OperationsEnterprise Intelligence Fabric + OntologyAI Applications + Models + AgentsData + Integration + APIsCloud + GPU + InfrastructureSecurity + Governance + Observability

How we engineer

A connected engineering model that takes growth from discovery to continuous adaptation.

  1. 01Discover
    • Enterprise landscape
    • Technology estate
    • Data and intelligence gaps
    • Opportunity map
  2. 02Architect
    • Target architecture
    • AI, data and cloud design
    • Integration model
    • Control plane
  3. 03Engineer
    • Products and platforms
    • Agents and models
    • APIs and pipelines
    • Infrastructure
  4. 04Industrialise
    • Evaluation harnesses
    • Agentic SDLC
    • MLOps and LLMOps
    • Repeatable patterns
  5. 05Deploy
    • Workflow activation
    • Digital workforce
    • Change and adoption
    • Release governance
  6. 06Operate
    • Service levels
    • Observability
    • Cost and FinOps
    • Incident and oversight
  7. 07Continuously adapt
    • Model and agent updates
    • Architecture evolution
    • Outcome measurement
    • Compounding value

Engineering stack

The disciplines this domain draws on most, from the complete engineering stack

Cloud Platforms

  • AWS
  • Microsoft Azure
  • Google Cloud Platform
  • Oracle Cloud
  • IBM Cloud
  • Alibaba Cloud
  • Cloudflare

Containers & Orchestration

  • Docker
  • Kubernetes
  • OpenShift
  • Helm
  • K3s
  • Rancher
  • Docker Compose
  • Istio
  • Envoy
  • Argo CD
  • Argo Workflows

Infrastructure as Code

  • Terraform
  • OpenTofu
  • Pulumi
  • AWS CloudFormation
  • Azure Bicep
  • Ansible
  • Chef
  • Puppet
  • SaltStack

Observability & Monitoring

  • Prometheus
  • Grafana
  • OpenTelemetry
  • Jaeger
  • Zipkin
  • ELK
  • Elastic Observability
  • Splunk
  • Datadog
  • New Relic
  • Dynatrace
  • Sentry
  • CloudWatch
  • Azure Monitor

Explore the full stack

Enterprise outcomes

Measured in the operating business, not in the programme report

Faster decision cycles

Reduce the distance between enterprise data and action.

Lower cost of operations

Automate the work itself, so cost per outcome falls as quality rises.

Higher engineering velocity

Accelerate product, platform and application development.

Greater automation

Automate work across processes, functions and workflows.

Reduced technology complexity

Modernise fragmented estates and simplify the enterprise architecture.

Improved enterprise visibility

See operations, risk and performance in one governed picture.

Scalable AI adoption

Move successful AI from individual use cases to enterprise-wide capability.

Continuous operating intelligence

An enterprise that improves without waiting for the next programme.

Frequently asked questions

Placement follows sovereignty, latency and cost. Most enterprises end up hybrid, and the platform is designed so workloads can move as those constraints change.

By treating it as an architectural constraint: model routing, caching, right-sized serving, GPU scheduling and FinOps visibility per model, per team and per request.

Yes. A platform team gives product teams governed, self-service access to models, data and tools, with the controls built into the platform rather than reviewed after.

The practice is platform-independent by design and works across the major hyperscalers, private cloud and on-premises estates, selecting according to the workload rather than a preference.

Build the intelligent enterprise. Explore an AI transformation opportunity with iFortis Worldwide®.

iFortis Worldwide®

Independently audited. Continuously governed.

ISO/IEC 27001:2022 certification mark

ISO/IEC 27001

Information security management

CERTIFIEDQUALITY MANAGEMENTISO9001

ISO 9001

Quality management

AICPA SOC 2 service organization control report

SOC 2 Type II

Security, availability and confidentiality

EU GDPR

GDPR

Data protection and cross border transfer

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