Services
The work around the hardware — which is most of the work.
An infrastructure decision is made once and lived with for four years. These are the services that decide whether it holds up: sizing it against the real workload, costing it honestly against cloud, building it, proving it, and running it.
How a Project Runs
| Phase | What Happens |
|---|---|
| 1 — Discovery | Business requirements, AI use case, technical environment, compliance constraints, budget, timeline. |
| 2 — Architecture | Workload analysis, sizing, hardware, network, storage, runtime and platform design. |
| 3 — Commercial Proposal | BOM, services, timeline, support, with assumptions and exclusions written down. |
| 4 — Build | Procurement, assembly, deployment, integration. |
| 5 — Validation | GenesysBench™ benchmarks, acceptance tests, workload tests. |
| 6 — Handover | Documentation, training, support transition. |
| 7 — Operate & Expand | Support, monitoring, upgrades, scaling, new workloads. |
Advisory & Architecture
Discovery & Sizing
“How many GPUs do we need” is the wrong first question, and answering it without the workload behind it is how organizations end up wrong in both directions at once — overspent on the wrong tier, underspecified on memory or fabric. Discovery starts from the workload: model sizes, precision, context length, concurrency, latency targets, data volume, growth curve. The sizing falls out of that.
Coversrequirements discovery, workload analysis, GPU/storage/network sizing, capacity planning, cloud-vs-on-prem analysis, growth planning.
TCO & Business Case
On-prem isn't automatically cheaper, and anyone who says otherwise is selling. It becomes cheaper above a utilization threshold — and that threshold moves with egress volume, model size, support model and depreciation schedule. We build the comparison with your numbers in it, including the cases where staying on cloud is the right answer.
Coverscloud vs on-prem TCO, GPU utilization economics, ownership cost, depreciation, energy, licensing, egress, sovereign AI value.
Architecture & Design
The architecture document is what a BOM gets built from — compute, fabric, storage and platform decided together rather than in sequence, with the trade-offs written down. It's also what makes a build reviewable before any hardware is ordered.
Coversinfrastructure architecture, AI platform architecture, cluster architecture, storage and network architecture, edge architecture, sovereign AI architecture, deployment design.
Build & Deploy
Data Centre Design & Build
Sovereign and enterprise-scale AI programs — AI Factory and the top of the HPC Sovereign tier — need a facility purpose-built around them, not a server order dropped into whatever space is available. An 8-phase program from first site assessment to handover of a running, validated facility.
| Phase | Name | What Happens |
|---|---|---|
| 1 | Discovery & Site Assessment | Power, cooling, and space audit against the target workload profile; compliance and sovereignty requirements captured up front. |
| 2 | Design & Engineering | Electrical, mechanical, network, and rack-layout design; redundancy tier (N, N+1, 2N) set to match the customer's uptime requirement. |
| 3 | Procurement & Vendor Coordination | Hardware sourcing and long-lead-item management across the NVIDIA, Intel, and AMD supply chains. |
| 4 | Civil & Facility Build-Out | Flooring, containment, power distribution, and cooling-plant installation — the physical shell the systems will sit inside. |
| 5 | Rack & Hardware Integration | Systems racked, cabled, and pre-tested on-site — the same GLIDS integration step used on every standalone deployment, at facility scale. |
| 6 | Network & Fabric Commissioning | Fabric bring-up and validation — InfiniBand or Ethernet, matched to the HPC/AI Factory tier — with redundancy paths tested, not assumed. |
| 7 | Validation & Load Testing | Full-facility GenesysBench™ pass under real load, plus failover and thermal/power validation before anything is called done. |
| 8 | Handover & Operations Support | Documentation, runbooks, and monitoring dashboards handed to the customer's team, backed by the GLIDS support SLA. |
Installation & Cluster Deployment
Hardware installation, cluster bring-up, fabric configuration, runtime install — Kubernetes or Slurm — GPU enablement, storage integration and monitoring, through to commissioning.
Rack Integration
Some customers already operate a data centre and need GLabs systems racked, cabled and validated inside it. Rack Integration is the Phase 5 + Phase 7 slice of the data-centre program, delivered on its own: site survey against existing power, cooling and rack-space constraints; racking, cabling and fabric bring-up to the existing network; a GenesysBench™ validation pass before handover.
Validate & Operate
Benchmarking & Validation
Acceptance shouldn't be “it powered on.” Every deployment clears a GenesysBench™ pass against the workloads it was sized for, with the results handed over as a document rather than a verbal assurance. This is also the step that turns a vendor relationship into a technical one.
Coversbenchmark automation, performance validation, workload-specific testing, GPU comparison, throughput, latency, scaling, cluster efficiency.
Monitoring & Observability
GPU utilization, job throughput, thermal and power headroom, cluster health — instrumented at handover rather than retrofitted after the first incident. Grafana, Prometheus and DCGM on the HPC stack; equivalent instrumentation on smaller deployments.
Coversinfrastructure and GPU monitoring, workload monitoring, health checks, capacity monitoring, cluster observability, alerting.
Maintenance & Support
Local and remote support, preventive maintenance, incident response, vendor coordination, component replacement and upgrade planning, under a defined SLA.
Platform Enablement
Sovereign AI Enablement
Sovereign AI means customer-controlled infrastructure, models, data and operations — not a hyperscaler region that happens to be in-country. For regulated industries, public sector, and anyone whose data cannot leave a jurisdiction or a building.
Coversdata residency and sovereignty, private and on-prem AI, secure LLM deployment, internal RAG, enterprise agents, regulated-environment deployment.
RAG, LLM & Agentic AI Enablement
The layer between working infrastructure and a working AI application: enterprise LLM deployment, local model serving, RAG pipelines, vector stores, inference optimization and agent platform integration. Application-layer development is delivered with software partners.
Coversenterprise LLMs, local models, RAG, inference, agentic AI, observability, evaluation, orchestration.
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