AI DATA CENTER · 04

Solutions that create value on top of infrastructure

Hardware is only the starting point for AI; what creates real value on top of it is software. We provide the entire operational software layer as a single solution — standing up the stack, sharing out resources, handling traffic, blocking threats, and proving performance.

Six Solutions

Six solutions that add value to infrastructure

The value of AI infrastructure isn't completed by a single function. From stack build to resource operations, observability, traffic control, protection, and validation — six solutions work together, interlocked.

• BUILD

  AI stack build

• OPERATE

  GPUaaS operations

• MONITOR

  Integrated monitoring

• CONTROL

  AI traffic control

• PROTECT

  Abuse prevention

• VALIDATE

  Performance & acceptance validation

In Detail

What each solution delivers

AIMTOG handles hardware operations, while the software and operations layers are delivered through the software and Kubernetes engineering of our affiliate, STCLab.

• BUILD

  AI stack build

  On top of GPU hardware, we stand up a software environment where workloads actually run. From cluster design to training and inference pipeline setup and handover.

  • Kubernetes cluster design, build, and handover
  • Orchestration and multi-cluster configuration (A/B site integration)
  • GPU scheduling — gang, queue, quota, fair-share
  • GPU virtualization and sharing environments
  • Distributed training pipeline configuration
  • LLM serving (inference) pipeline configuration
  • Installation and integration of NVIDIA GPU management software
  • GitOps-based deployment pipeline
  K8s engineering (KCSP)

• OPERATE

  GPUaaS operations

  Share and bill GPUs like a service. We reclaim wasted resources and redistribute them by usage to drive up real utilization.

  • GPU virtualization — vGPU partitioning, hard isolation, memory overcommit
  • Priority QoS — non-disruptive preemption and automatic resume
  • Reclaiming wasted GPUs and re-sizing vGPUs
  • Tenant chargeback framework
  • Diagnose-to-auto-recover — hardware, thermal, and memory faults
  • Resource defragmentation and capacity forecasting
  • vGPU sales-unit and tenant-quota design
  • SLA-tier-based operations
  Wave Autoscale

• MONITOR

  Integrated monitoring

  See GPUs, servers, and even cooling equipment on a single screen. Built on open source and unified so tools don't scatter.

  • Metric collection and long-term storage
  • Unified observability through a single dashboard
  • Continuous GPU telemetry collection
  • Node and hardware status observation (BMC · Redfish)
  • Integration with liquid-cooling and facility equipment (CDU)
  • Unified observation across A/B sites
  • Log collection and analysis integration
  • Threshold-based alerting
  Open-source integration

• CONTROL

  AI traffic control

  Keep the service from collapsing under a surge of requests. We control inference admission based on GPU saturation to maintain response quality.

  • Admission-rate control and queue management
  • Inference admission control based on GPU saturation
  • Priority tiers — guaranteed response time for key requests
  • Dynamic control based on response time
  • Virtual waiting room — order and estimated wait shown at saturation
  • Model-aware routing and overflow handling
  • Per-tenant token-budget guards — preventing cost runaway
  • Segment-level and outbound traffic control
  NetFUNNEL API

• PROTECT

  Abuse prevention

  Filter out junk requests before they reach the GPU. Blocking at the front translates directly into savings in GPU cycles and cost.

  • Bot and macro blocking — a three-stage, multi-layer defense
  • Front-of-GPU blocking to cut compute cost
  • OWASP-based web-attack detection and blocking
  • Account-takeover defense
  • Countermeasures against model scraping and distillation attacks
  • Detection of stolen API-key usage
  • Agentic-traffic classification — identifying legitimate AI agents
  • AI-based automated detection and response
  BotManager

• VALIDATE

  Performance & acceptance validation

  We prove, in numbers, that the build is done. Before handover, we run standardized performance validation and provide the results as a report.

  • Acceptance and performance validation with a results report
  • Script-free capture and replay automation
  • Zero-load capture of real traffic (minimal operational impact)
  • Snapshots turned into assets after PII masking
  • LLM performance metrics — time-to-first-token and throughput
  • Multi-tenant resource-contention testing
  • Disaster-recovery and failover validation (A → B)
  • Functional-validation automation
  LoadTester

Foundation

What underpins these solutions

Solutions aren't completed by products alone. A certified engineering organization, proven products, and a hardware maintenance framework all stand behind them.

• CERTIFIED

  CNCF KCSP certified

  An engineering organization holding Kubernetes Certified Service Provider status performs cluster design and operations.

• HARDWARE

  HPE Service Delivery Partner

  AIMTOG performs hardware maintenance directly, from routine inspection to fault recovery and parts procurement.

• OPEN SOURCE

  Open-source-based configuration

  Configured on an open-source stack free of vendor lock-in, securing long-term operational flexibility.

A Operating AI infrastructure can't be completed by hardware or software alone. AIMTOG combines its own work with the software and operations-layer capabilities of STCLab — an affiliate under the technology holding company BasecampG — to provide a management framework that connects seamlessly from equipment deployment through service operations.

We'll review which solution you need — together with you.