SERVICES

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.

Facility-scale builds run a longer, 8-phase version of phases 3–6 — see Data Centre Design & Build below.

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.

Covers: requirements 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.

Covers: cloud 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.

Covers: infrastructure 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.

Rendered view down a data-centre aisle between cabled server racks

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.

Covers: benchmark 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.

Covers: infrastructure 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 cloud region with a European flag on it. For regulated industries, public sector, and anyone whose data cannot leave a jurisdiction or a building.

Covers: data 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.

Covers: enterprise LLMs, local models, RAG, inference, agentic AI, observability, evaluation, orchestration.

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