From application to infrastructure.
AI is more than calling an API. A system that works in a notebook and a system that runs every day for a business are different engineering problems.
We work across the whole AI stack: the application a business uses, the retrieval and orchestration behind it, the serving layer that makes it fast enough, and the infrastructure that makes it affordable.
That range matters because most AI problems are misdiagnosed. Slow responses are usually a batching or placement problem, not a model problem. Poor answers are usually a chunking or metadata problem, not a model problem. Cost overruns are usually a routing and caching problem.
The stack we work across
- AI ApplicationsProducts and workflows people actually use
- LLMs · RAG · Agents · AutomationRetrieval, orchestration, tool use, evaluation
- Model Serving & InferenceThroughput, latency, batching, quantisation
- AI PlatformsPipelines, registries, experiment and cost tracking
- Kubernetes & ContainersScheduling, isolation, autoscaling, multi-tenancy
- GPU · Compute · StorageAccelerators, throughput, capacity planning
- Cloud · Private Cloud · DatacenterPublic, hybrid, on-premise, colocation
- Networking · Security · MonitoringSegmentation, identity, observability
Where AI projects actually stall
- A prototype answers well on ten documents and poorly on ten thousand.
- Latency is acceptable in testing and unacceptable under concurrency.
- Inference cost scales linearly with usage and nobody modelled it.
- A model update changes behaviour and no evaluation suite catches it.
- Retrieval returns plausible passages that do not answer the question.
- Data residency rules rule out the hosted API after the build has started.
- GPUs sit idle at a third of capacity because scheduling was an afterthought.
Application and infrastructure
Slow responses are usually a batching or placement problem. Poor answers are usually a chunking or metadata problem. Cost overruns are usually a routing and caching problem. Diagnosing any of them requires seeing both layers at once.
Capabilities
Strategy & applications
- AI strategy and opportunity assessment
- Generative AI and LLM applications
- Retrieval-augmented generation
- AI agents and orchestration
- AI-driven process automation
- Application development and integration
Models & serving
- Model serving and inference optimisation
- Training and fine-tuning infrastructure
- Quantisation, batching and throughput tuning
- Model routing across hosted and self-hosted
- Evaluation harnesses and regression testing
Platform & infrastructure
- GPU infrastructure and capacity planning
- Self-hosted and private AI deployments
- Kubernetes-based AI platforms
- Pipelines, registries and artefact management
- Multi-tenant scheduling and isolation
Operations
- AI observability and tracing
- Cost optimisation and token governance
- AI security and data boundaries
- Human-in-the-loop review and approval
- AI-powered QA and test generation
We understand the AI application layer and the infrastructure layer underneath it. Most teams have one or the other.
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Complex problems rarely fit inside one technology. Tell us what you are trying to solve and we will tell you how we would approach it.
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