Datacentre Infrastructure and Networks for AI

Infrastructure is now strategic, not just operational. We know budgets are tight and teams are stretched. AI workloads, hybrid cloud, sustainability and security mandates necessitate platforms and networks that are agile, resilient and efficient. SCC designs and manages these with vendor-agnostic expertise to simplify complexity, reduce risk and future-proof outcomes.

Make infrastructure explicitly AI ready with high throughput fabrics, SD WAN/SASE governance, GPU ready nodes and secured edge processing that keeps inference close to data.

Plan with SCC’s decision tools

Refresh or re-platform

Model TCO, ROI and performance uplift for mixed CPU/GPU estates, then choose the right landing zone for AI-era workloads.

Open the calculator

VMware renewals, without the guesswork

Apply renewal strategies that reduce risk, maintain compliance and keep options open across hosted private, public and hybrid choices.

The changing landscape of infrastructure

Modern enterprises must cut cost while expanding capacity, meet compliance while enabling collaboration and modernise without disruption. Networks need speed, intelligence and resilience. Datacentres must be energy-efficient and scalable, blending cloud-native agility with enterprise-grade reliability.

AI multiplies demand for bandwidth and compute. With well-architected networks and optimised infrastructure from edge to core you get secure performance, lower latency and freedom from lock-in. This sets out the patterns that make AI production-ready across network, datacentre and edge.

High performance compute that’s optioned to fit

Not every AI, engineering or quant workload belongs on commodity cloud. We design the right mix with you across burstable partner platforms, hosted or private DC builds and hybrid patterns, then align density, power and cost to how you actually run. The calculator models break-even points and scale plans so decisions are evidence-based.

SCC can offer:

Placement choices: public, hosted private or on-prem, guided by data gravity, latency and egress
Performance architecture: GPU and CPU mix, high-speed interconnects, and parallel storage sized to dataset and job profile
Operations: schedulers, observability and resilient runbooks that keep clusters busy
Economics and sustainability: TCO including energy, cooling and licensing, with carbon reporting and power-aware design

Discuss HPC optioning

Retail edge analytics at scale

Challenge

High-theft, high-volume retail environments produced massive 4K video streams that were too costly and slow to backhaul to cloud, undermining loss prevention and customer experience initiatives.

Existing camera vendors pushed expensive, compute-heavy devices that locked retailers into siloed analytics, multiplying cost and complexity across stores and use cases.

What SCC did

Designed an edge compute blueprint: standardised “dumb” cameras feeding secure on-site GPU servers, with SD-WAN/SASE with central policy and analytics. Processed locally, governed centrally and only shipped alerts and metadata upstream. Enabled multi-tenant analytics on the same edge feed for loss prevention, safety and footfall journey mapping. Retailers can add new AI providers without rip-and-replace hardware or cloud egress cost spikes.

Outcomes

Enabled faster in‑store alerting and intervention, while deterring walk‑outs without invasive customer handling. Network costs fell by avoiding bulk video backhaul. Procurement shifted from premium smart cameras to standardised devices plus shared edge compute, reducing capex exposure and avoidable vendor lock‑in across estates.

Why it matters

Provides a scalable pattern for AI at the edge: faster outcomes, lower cost and safer governance. Extendable to rail safety, facilities security and smart shelving, all anchored in robust network design.

Intelligent digitisation for AI discovery

Challenge

Organisations held decades of critical knowledge on paper, trapped in warehouses with recurring storage charges and no practical way to search, correlate or share insights such as public inquiries, healthcare records and legal discovery. Previous scan-to-PDF approaches created static archives, compliant but not interrogable, which forced manual review cycles that delayed outcomes and increased legal and operational risk.

What SCC did

Engineered an end-to-end physical-to-intelligent pipeline: secure digitisation, governed data patterns and AI-ready indexing that surfaces page-level facts to prompts while preserving provenance for audit and disclosure.
Deployed the pipeline alongside AI-ready infrastructure patterns so teams could run search and retrieval locally or in hybrid, aligned to zero-trust controls and sector compliance obligations.

Outcomes

Legal and public sector teams accelerated targeted retrieval versus manual review, with defensible chains of custody for every document and query. Reduced storage and logistics overheads as archives were decommissioned. Data access expanded safely to authorised roles, enabling new research and cross-agency collaboration at lower risk and cost.

Why it matters

Unlocks institutional memory and enables AI to work on what was previously invisible, tied directly to SCC positioning on AI-ready networks, secure datacentre patterns and compliance-by-design architectures across edge to cloud.

Opportunities, key challenges and SCC solutions

Insights & Resources

SD-WAN ROI: How to Quantify Cost Savings and Operational Efficiency

In today’s hybrid and cloud-first business landscape, network agility and cost efficiency are no longer optional – they’re essential. Software-Defined Wide Area Networking (SD-WAN) has emerged as a transformative solution, enabling organisations to modernise their connectivity while delivering measurable financial and operational benefits, whilst underpinning their business in the right way. But how do you…