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CIFL

Project Management Consultancy

Project consultancy across the infrastructure stack.

CIFL Consult advises on, plans, and governs infrastructure programs. We are not a managed-service operator. The work is to leave your team with architecture and delivery they can own.

  1. Data hall with server cabinets and warm aisle lighting used to illustrate cloud program consultancy
    01

    Cloud Program Consultancy

    Architecture advisory and migration program management across AWS, Google Cloud, and Microsoft Azure.

    We structure cloud programs around landing zones, networking, security architecture, and multi-cloud governance — so delivery stays sequential, reviewable, and owned by your team.

    • AWS
    • Google Cloud
    • Microsoft Azure
    • Landing zones
    • Networking
    • Security architecture
  2. Engineering workstations with dual monitors showing delivery pipelines
    02

    Platform & DevOps Program Management

    IaC, CI/CD, and GitOps programs designed so infrastructure stays reproducible as teams grow.

    We advise on Terraform standards, Kubernetes platform roadmaps, environment management, and delivery pipelines. The outcome is a program your engineers can run — not a platform CIFL operates.

    • Terraform
    • Kubernetes
    • Docker
    • GitHub Actions
    • Argo CD
    • GitOps
  3. Operations room with monitoring walls illustrating reliability program advisory
    03

    Reliability Program Advisory

    SRE operating models, observability strategy, and resilience planning for systems that cannot afford guesswork.

    Incident process design, HA/DR planning, distributed tracing, and performance review — framed as a reliability program with clear owners, signals, and review cadences.

    • SRE
    • OpenTelemetry
    • Prometheus
    • Grafana
    • Incident management
    • Disaster recovery
  4. Dense GPU compute racks with amber status lighting
    04

    GPU & AI Infrastructure Consultancy

    Program management and architecture advisory for GPU platforms, inference, and cost-aware model serving.

    Running AI in production is a delivery problem: scheduling, throughput, latency, utilization, networking, and cost. We help you plan that program before spend and complexity compound.

    • GPU orchestration
    • LLM inference
    • Model serving
    • Autoscaling
    • HPC
    • Inference optimization
  5. Conference table and screens used to illustrate cloud economics and controls
    05

    Cloud Economics & Controls

    FinOps advisory, rightsizing programs, and GPU cost governance as a structured project.

    Capacity planning and architecture optimization with explicit controls. No invented savings figures — the work is to make spend explainable and decisions reversible.

    • FinOps
    • Rightsizing
    • GPU cost governance
    • Capacity planning
    • Architecture review
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