Private AI Lab
We solve AI problems
NPU Labs develops, trains, evaluates, secures and operates AI technology.
We combine custom model engineering, private compute, enterprise software expertise and rigorous evaluation to deliver systems whose performance, behaviour and origins can be examined and verified. From research and experimentation through to production deployment, we provide the technology, engineering evidence and operational knowledge needed for customers to retain control of their AI systems.
Come to NPU Labs with a problem, and we can investigate whether it needs a custom-trained model, a smaller optimised model, a conventional software solution, an agentic system, or a combination—and prove why the chosen solution is appropriate.
Infrastructure
Private AI compute you own. NVIDIA DGX Spark systems and workstations, on-prem or hybrid. Specified, installed, and maintained for you.
Private Lab
A private ML lab on hardware you own: a workbench for experimentation, fine-tuning, embeddings, and evaluation, with MLOps on your local stack.
AI Engineering
AI in production: internal platforms, agents, RAG and knowledge systems, observability, workflow automation, and system integration.
AI Guardrails
Policy enforcement for developer AI tools and agents, across IDEs, repositories, CI/CD, MCP servers, and model gateways.
Services
NPU Labs designs, builds, trains, evaluates, secures, and operates AI and software systems. We offer five services. Each can be commissioned on its own or combined with others for a larger programme.
AI model training, inference, and evaluation
A trained or optimised model, with measurable, reproducible evidence.
- Custom training, fine-tuning, and distillation
- Inference optimisation on CPU, GPU, and NPU (neural processing unit)
- Benchmarks, evaluation, and regression testing
- Data lineage and provenance
AI infrastructure and platform engineering
A deployed AI environment your team can operate.
- Private GPU infrastructure, including NVIDIA DGX Spark
- Self-hosted models and inference endpoints
- AWS, Azure, and hybrid set-ups
- Kubernetes, CI/CD, monitoring, and runbooks
AI-native cybersecurity
Security risks found, validated, fixed, and kept under test.
- Application, API, cloud, and container security
- Vulnerability management and retesting
- Red, blue, and purple teaming
- Security testing of models, agents, RAG, and Model Context Protocol (MCP) servers
AI governance, strategy, and assurance
Policies, controls, and evidence for running AI responsibly.
- AI strategy and use-case prioritisation
- Policies, guardrails, and human oversight
- Risk assessments and compliance alignment
- Evaluation frameworks and quality gates
Enterprise software and agentic engineering
Software, AI applications, and agents running in production.
- Enterprise applications, APIs, and integrations
- Conversational and voice agents with human escalation
- Autonomous agents and governed workflows
- Testing, CI/CD, and production reliability
Why clients choose NPU Labs
We work below the API layer and carry the evidence through to production. Rather than starting from an off-the-shelf model or platform, we work out what the workload needs: a custom-trained model, an optimised small model, conventional software, an agentic system, or a combination. Other companies have these capabilities too. We bring them together in one practice, with technical accountability and a traceable handover.
- 01
Engineering beyond integration
Our work goes below calls to commercial AI services: model training, inference, decision engines, orchestration, APIs, and operating platforms. Flash, Neuro, and our autonomous runtimes are examples.
- 02
Evidence rather than demonstrations
Defined evaluation criteria, benchmark methods, baseline comparisons, versioned experiments, dataset lineage, and provenance. Where datasets and permissions allow, results can be reproduced.
- 03
Deployment freedom
Private GPU infrastructure and self-hosted models, as well as AWS and Azure. Your constraints decide the architecture. No cloud or model vendor is mandatory.
- 04
Assurance built into delivery
Guardrails, evaluation, policy checks, security testing, regression suites, monitoring, and human oversight are designed in from the start, not added at the end.
- 05
Senior, hands-on engineering
Experience in enterprise software, distributed architecture, reliability, automation, and production operations, applied to AI research and delivery.
Our Products - based on research
Evidence-based
Every substantive recommendation we make is tied to evidence suited to the engagement: a working implementation, a controlled experiment, a measured benchmark, a test result, a documented architecture, or an operational record. We keep measured results apart from assumptions and proposals, and we don't present products still in development as independently validated customer outcomes.
We agree the deliverables for each engagement. Where relevant, they include the documented method, source and version records, evaluation evidence, configurations, instructions to reproduce the results, and an operational handover.
NVIDIA
Qwen
Mistral AI
Meta Llama
DeepSeek
Google Gemma
OpenAI
Claude
Hugging Face
PyTorch
Python
Ollama
LangChain
Qdrant
PostgreSQL
Docker
Kubernetes
Ubuntu
MCP
Grafana
Prometheus