Data Platforms for AI/ML Solutions
Build the data foundation that makes AI deliverable. GPU and CPU mixes sized to your actual workloads, parallel storage matched to dataset and job profile, governed pipelines from edge to core. Vendor-agnostic design across public cloud, hosted private and on-premises.
Why it matters
AI initiatives rarely fail because the model is wrong. They fail because the data platform underneath cannot feed it. Data sits in the wrong format, in the wrong place, under the wrong governance. Pipelines are bolted together. Storage cannot keep GPUs busy, so expensive compute waits on slow disks. Costs scale faster than the value being captured. By the time the second model goes into production, the platform is already a liability rather than a foundation.
The economics make the problem worse. AI workloads multiply demand for bandwidth, compute and storage in patterns that legacy estates were not designed for. Mixed CPU and GPU profiles, dataset sizes that change by orders of magnitude across the lifecycle and inference latencies that depend on where the data physically lives. Public cloud is sometimes the right answer, sometimes the most expensive one. Egress charges punish bad placement decisions. Data sovereignty obligations remove options entirely. License cost shock from incumbents like VMware adds another variable to the planning. The teams making these decisions are stretched, and the skills market is tight.
Most organisations also hold institutional knowledge that AI cannot reach. Decades of paper records, image archives, scanned PDFs, voice recordings, video streams. The information is there. It is not interrogable. Static archives created by previous scan-to-PDF programmes are compliant, but they cannot answer questions. Manual review cycles delay outcomes and increase legal and operational risk.
SCC designs and runs the data platforms that make AI work as a production capability. We size compute and storage to the actual dataset and job profile, design the high-speed fabrics that keep GPUs productive and place workloads where the data gravity, latency and egress economics make sense. We deploy schedulers, observability and runbooks that keep clusters busy. Where institutional data sits in physical or static form, we engineer the physical-to-intelligent pipeline that gives AI something to work on. The approach is vendor-agnostic, so the design serves the workload, not the vendor.
How it works
Step 1
Map the workloads, the data and the constraints
We start with the AI workloads you are running, the data they need, where it lives, what compliance applies and what good looks like commercially. The output is a clear picture of dataset sizes, job profiles, latency tolerances, sovereignty obligations and the existing platform reality.
Step 2
Model placement and TCO with the calculator
Using SCC’s refresh or re-platform calculator and the VMware renewal guide, we model the realistic options across public cloud, hosted private and on-premises. Outputs are TCO including energy, cooling and licensing, performance projections and break-even points your finance team can sign off.
Step 3
Design the platform
GPU and CPU mix, fabric, parallel storage, scheduler, observability stack, governance pattern. Each design decision is driven by the workload analysis, not the catalogue. Power-aware design and carbon reporting are built in.
Step 4
Build, integrate and govern
We deploy the platform, integrate it with your existing identity, security and observability stack, then apply the governance pattern that suits your sector. Where intelligent digitisation is part of scope, we design and stand up the physical-to-intelligent pipeline alongside the platform.
Step 5
Operate and optimise
Once live, we operate the platform to defined service levels via SCC’s operations centre, with continuous optimisation against utilisation, cost and carbon reporting. The platform evolves with the workload mix rather than being rebuilt every refresh cycle.
Partners
GPU platforms, networking and software stack at the centre of most AI training and inference designs. Partnership covers reference architectures, sizing tools and supply.
Compute and storage platforms used for AI and HPC workloads, including GPU-optimised systems and parallel storage architectures.
Compute, storage and networking deployed across AI training and inference environments, including converged and hyperconverged options for mixed CPU and GPU estates.
High-performance storage designed for AI training pipelines, with parallel access patterns sized to dataset and job profile.
Public cloud AI infrastructure including GPU instances, AI services and integration patterns for hybrid placement of training and inference workloads.
Public cloud AI infrastructure including GPU and accelerator instances, managed AI services and integration patterns for hybrid placement.
Awards and accreditations
ISO 9001 (Quality Management)
Documented service delivery, change control and incident management. Translates into predictability for clients running mission-critical AI workloads.
Carbon Net Zero by 2050 and UN Race to Zero
SCC has set Carbon Net Zero targets for 2050 and supports the UN Race to Zero campaign. Design and operations include power-aware decisions, energy reporting and carbon tracking aligned to ESG mandates and net-zero roadmaps.
Get the data platform decision right before the model decision
If you are scaling AI from pilot to production, planning a refresh or facing VMware renewal, the data platform call is the one that locks in cost and risk for the next three years. We can model your options with the calculator, walk you through the placement framework and show you what an AI-ready data platform looks like for your workload mix.
FAQs
What makes a data platform AI-ready, beyond having GPUs?
GPUs are the visible part. The data platform underneath decides if they stay productive. AI-ready means high-throughput, low-latency fabrics so the data can keep up with the compute, parallel storage sized to dataset and job profile, GPU and CPU mix matched to the workload patterns and inference paths kept close to the data they serve. Governance, observability and scheduler design matter as much as the hardware: a cluster that idles or fails opaque is expensive, not productive.
Should we run AI workloads on public cloud, hosted private or on-premises?
All three play a role. Placement is guided by data gravity, latency tolerance, egress economics, sovereignty obligations and the steady-state cost of utilisation. Burstable partner platforms cover spikes well. Hosted private or on-premises tends to win for steady high-utilisation work, regulated data or workloads where egress would dominate the bill. The calculator models the break-even points so the call is made on evidence rather than vendor pressure.
What is capacity requirement planning for AI workloads, and why does it matter?
It is the discipline of forecasting compute, storage and network demand against growth, peak training events and real-time inference patterns. The discipline also aligns placement and budget to the SLA, resilience and security targets. Without it, organisations either over-provision (paying for capacity that idles) or under-provision (training jobs queue, inference latencies break SLA, model rollouts slip). With it, decisions are evidence-based and the platform evolves with the workload.
We are facing VMware renewal. How does that affect AI platform decisions?
Renewal is a forcing function rather than a disaster. It surfaces the cost of doing nothing and opens the question of if the next three years of compute should sit on the same stack. SCC’s VMware renewal guide compares hosted private, public and hybrid options against retention, with explicit treatment of AI workload requirements. The call is rarely ‘stay’ or ‘leave’ in absolute terms. It is which workloads belong where.
We have decades of paper archives that AI cannot reach. Is digitisation actually worth it?
It depends on if the data is operationally valuable. For legal discovery, public inquiries, regulatory case files and clinical records, the answer is usually yes: SCC’s intelligent digitisation pipeline turns those archives into AI-queryable knowledge with page-level facts surfaced to prompts and full provenance preserved for audit. Targeted retrieval accelerates against manual review, storage and logistics overheads come out of the run rate and access can be expanded safely to authorised roles. For purely archival material with no operational value, scan-to-PDF is enough.