IT Infrastructure Engineered for Artificial Intelligence

Why London’s AI Startups Choose Nerdster

The AI startup landscape in London has shifted decisively. In 2025 alone, UK AI startups raised over £5.8 billion, with London accounting for more than 60% of that capital. But with investment comes expectation — and the infrastructure demands of modern AI companies bear no resemblance to a typical SaaS startup.

Your ML engineers need GPU clusters that scale on demand. Your training data requires enterprise-grade encryption. Your investors want SOC 2 compliance. Your enterprise prospects require ISO 42001 certification. And the EU AI Act has introduced regulatory obligations that did not exist two years ago.

Nerdster provides IT infrastructure for AI startups that addresses every one of these challenges — so your team focuses on building models, not managing servers.

GPU Infrastructure That Scales With Your Ambitions

The single biggest infrastructure challenge for AI startups is compute. Training runs that require hundreds of GPU-hours cannot wait for procurement cycles, and inference endpoints that serve millions of requests need reliable, low-latency infrastructure.

We manage GPU compute across every major cloud provider — AWS, Azure, GCP, CoreWeave, Lambda, and specialist GPU hosting platforms. Our approach optimises three dimensions simultaneously: cost (ensuring you are on the right instance type and pricing model), availability (reserving capacity for critical training windows), and performance (matching hardware to workload characteristics).

When your Series A closes and you need to 10x your training capacity, we scale your infrastructure in days, not weeks.

Securing the AI Pipeline End-to-End

AI startups face a unique security surface. Your valuable assets are not just customer data — they are training datasets, model weights, fine-tuning data, and inference APIs. A breach that exposes model weights or proprietary training data can destroy your competitive advantage overnight.

Our AI security approach covers the entire pipeline: data ingestion encryption, secure training environments with access logging, model weight protection through hardware security modules and encrypted storage, and inference endpoint hardening with rate limiting and authentication. We implement data lineage tracking so you know exactly where every training sample originated and who has accessed your models.

AI Governance Is No Longer Optional

The regulatory landscape for AI has transformed. The EU AI Act is now in force, with obligations ranging from transparency requirements for general-purpose AI to strict compliance frameworks for high-risk systems. ISO 42001 has emerged as the international standard for AI management systems. And enterprise buyers increasingly require evidence of responsible AI practices before signing contracts.

We help you navigate this landscape pragmatically. Our compliance programmes cover risk classification under the EU AI Act, ISO 42001 readiness, NIST AI Risk Management Framework alignment, and SOC 2 Type II certification. We build the governance infrastructure — policies, technical controls, audit trails, and bias monitoring systems — that satisfies regulators and unlocks enterprise sales.

MLOps Infrastructure That Does Not Break at Scale

The gap between a model that works in a Jupyter notebook and one that serves production traffic reliably is enormous. Most AI startups hit this wall around Series A, when the pressure to ship production features outpaces the team’s ability to maintain infrastructure.

We build and manage MLOps infrastructure that bridges this gap: experiment tracking, model registries, CI/CD pipelines for model deployment, A/B testing frameworks, monitoring and alerting for model drift, and automated rollback when performance degrades. Your engineers ship models. We make sure those models run reliably in production.

The Nerdster Advantage for AI Startups

We are not a generic IT company learning about AI alongside you. Our engineers understand the difference between training and inference workloads, know when to recommend spot instances versus reserved capacity, and can explain to your auditor why your GPU cluster needs different security controls than a standard web application.

From your first prototype to your Series B infrastructure build-out, Nerdster provides the IT foundation that lets you focus on the science and the product — not the plumbing.

If your current IT provider does not know what a model registry is, it is time to talk to Nerdster.