AI infrastructure for your industry
The same AI infrastructure challenge looks different in every sector. We've built propositions across eight industries, each grounded in real delivery experience across 255+ client references.
Financial Services & Insurance
Banking, capital markets, mortgages and insurance, investment banks, challenger banks, mortgage lenders, Lloyd's market insurers and financial software providers.
Data sovereignty requirements prevent use of public cloud AI. Model risk governance and FCA/PRA oversight require fully auditable AI usage. High compute costs for quantitative AI, real-time risk and fraud detection. Multiple vendor contracts with different renewal dates.
Private AI environments deployed on-premise or in UK sovereign colocation. Zero-trust networking. AI governance framework aligned to FCA expectations and Model Risk Management guidance. GPU infrastructure for quantitative training and real-time inference. Co-terminated support across the estate.
Regulatory-compliant AI without sacrificing performance. Fully auditable model access and data handling. 30–40% lower compute cost vs public cloud for sustained workloads. Single co-terminated contract replacing multiple vendor agreements.
Service Providers, Telco & ISPs
ISPs, telcos, network operators, data centre providers and MSPs across the UK — the largest single vertical in Moksha's base.
Customers demanding AI capability the provider can't deliver. Network infrastructure not optimised for AI traffic and GPU workloads. Pressure to differentiate against hyperscalers and larger VARs. Complex multi-vendor environments with fragmented support.
White-label AI capability via Moksha's channel programme. Network re-architecture for AI traffic patterns, including spine-leaf fabrics and 400/800GbE. Joint go-to-market on AI infrastructure and managed services. Co-terminated multi-vendor contracts.
New AI revenue stream without internal capability build. Network designed for AI workloads from day one. Differentiated proposition vs hyperscalers. Single point of accountability across vendors.
NeoClouds & GPU-as-a-Service Providers
Emerging UK and European NeoClouds and sovereign GPU operators standing up multi-megawatt AI capacity for training and inference customers.
Scaling GPU capacity faster than internal engineering teams can design and deploy it. Training workloads demand non-blocking fabrics and rail-optimised topologies that most teams haven't built before. Liquid cooling and power density planning push facilities to their limits. Multi-tenant isolation and noisy-neighbour control are critical but hard to get right. Margin pressure from underutilised GPUs and volatile token economics.
Reference architectures for current-generation NVIDIA clusters (B200/B300), with design patterns extending to next-generation platforms as they reach availability. Non-blocking InfiniBand and RoCEv2 fabrics with rail-optimised topologies. Storage tiers tuned for checkpointing and dataset throughput. Proven tenant isolation patterns across network, scheduler and observability layers. Vendor-neutral selection across NVIDIA, AMD and emerging silicon.
Faster time-to-revenue on new GPU capacity. Higher sustained utilisation and stronger gross margins. Infrastructure that's ready for the next generation of silicon without a redesign. Differentiated proposition vs hyperscalers on sovereignty, price/performance and support depth.
Media & Broadcast
Broadcasters, post-production, sports and streaming clients, including organisations operating real-time AI workflows at scale.
Real-time inference for live production. Massive media datasets require high-throughput storage and network. GPU capacity for generative content tools. Hybrid working across studios and remote teams.
GPU compute clusters tuned for media workloads. High-throughput storage and AI network fabric. Hybrid cloud architecture for burst capacity. Secure remote workflows with model access controls.
Production-ready AI for live and post environments. Predictable cost on burst-heavy workloads. Faster turnaround on AI-assisted content pipelines.
Education & Research
UK universities and research institutions running computational AI workloads, including HPC and large-scale training.
Diverse research workloads competing for shared GPU capacity. Funding pressure on infrastructure. Complex governance around research data and IP. Skills gap in AI infrastructure operations.
Shared GPU environments with workload scheduling and chargeback. Hybrid cloud for burst workloads. Research data governance frameworks. Managed operations to free internal teams.
More research output per GPU hour. Predictable cost across faculties. Compliant handling of research data. Reduced operational burden on internal IT.
Manufacturing
Manufacturers across automotive, industrial, food & beverage and engineering sectors deploying AI on the factory floor and across supply chain.
Edge inference at scale across multiple sites. OT/IT convergence and security. Latency-sensitive workloads (vision, predictive maintenance). Variable connectivity to central cloud.
Edge AI infrastructure tuned for OT environments. Site-to-cloud network design with resilience. Zero-trust segmentation between OT and IT. Centralised AI control layer across sites.
Reliable inference at the edge with central control. Reduced unplanned downtime. Auditable AI in safety-critical environments.
Legal & Professional Services
UK law firms and professional services organisations adopting AI for document review, knowledge management and client delivery.
Confidential client data prevents use of public AI services. Partner-led governance over AI usage. Need to demonstrate AI capability to clients without exposing data.
Private AI environments with strict data segregation. Model access controls aligned to client-matter boundaries. Audit trails for partner oversight. Hybrid architecture for selected workloads.
AI-enabled service delivery without confidentiality risk. Partner-grade governance and auditability. Differentiated client proposition.
SaaS & Technology
UK and international SaaS providers embedding AI features into their products, including PE-backed scale-ups.
Inference cost eroding gross margin. Multi-tenant AI architecture complexity. Rapid model iteration cycles. Pressure to ship AI features ahead of competitors.
Inference architecture optimised for unit economics. Multi-tenant GPU and isolation patterns. CI/CD for model deployment. FinOps automation for AI spend.
Higher gross margin on AI features. Faster model iteration. Predictable AI cost as the business scales.