Self-Hosted AI Infrastructure

Commercial AI APIs solve a real problem: they make powerful language model capabilities available without requiring infrastructure expertise. But they create a data problem that most businesses have not fully reckoned with: every query, document, and piece of proprietary information sent to a third-party inference API potentially contributes to training data, is subject to the vendor's data handling practices, and is stored in infrastructure you do not control.

For businesses handling client data, proprietary intellectual property, internal communications, financial records, or any category of information subject to confidentiality obligations, routing that information through a third-party AI API creates exposure that is difficult to remediate once the practice is established. Employees adopt AI tools quickly when they are easy to use. Governance of what data flows where is harder to establish after the habit is formed.

Self-hosted AI infrastructure addresses this at the infrastructure level: the models run in your environment, queries stay on your network, and proprietary data never leaves your control.

Self-hosted AI is not a single system — it is a stack of components that work together to provide AI capabilities within a controlled environment. We design and deploy these stacks based on your specific use cases and infrastructure constraints.

Language model inference is the foundation: deploying and operating one or more open-weight models (such as Llama, Mistral, or Qwen variants) on appropriate hardware, with an API layer that can serve requests from internal tools and applications. Model selection depends on your use case requirements, the hardware available, and the capability-efficiency trade-offs relevant to your context.

Retrieval-Augmented Generation (RAG) infrastructure connects your document corpus, knowledge base, or internal data to a language model. This allows the model to answer questions about your specific business context — your products, your contracts, your internal processes — rather than only its training data. The pipeline covers document ingestion, chunking, embedding, vector storage, and retrieval.

Internal tooling interfaces expose AI capabilities to your team through appropriate interfaces — internal chat tools, document analysis pipelines, code assistance environments, or custom integrations with your existing systems.

Language model inference requires GPU resources, and the appropriate hardware configuration depends on the models you want to run, the concurrency requirements of your team, and your hosting environment. We assess these requirements during the architecture phase and design an infrastructure configuration appropriate to your constraints.

For businesses running cloud infrastructure, GPU instances are available from most major cloud providers and can be right-sized for your specific model requirements. For businesses with on-premises hardware, we can work with existing GPU resources or specify appropriate hardware additions.

Not all AI use cases require large models with significant hardware requirements. Many valuable internal AI applications — document classification, structured data extraction, summarisation of well-scoped content — can be served by smaller models that run efficiently on CPU resources. We will be direct about what you actually need rather than defaulting to the largest and most expensive option.

AI infrastructure has operational characteristics that differ from conventional application infrastructure. Models are large and take time to load. Hardware utilisation patterns are spiky and difficult to predict. Model versions evolve and need to be evaluated before production deployment. Context window management affects both performance and output quality.

Ongoing operations for AI infrastructure covers these specific concerns in addition to standard infrastructure operations: model version monitoring, performance benchmarking across model versions, hardware utilisation optimisation, and coordination of model updates with the teams that depend on them.

We also document the specific configuration of your AI stack in enough detail that the context does not disappear when personnel change. The models deployed, the retrieval configuration, the prompt templates, the hardware provisioning — all of this is documented and maintained as part of the operational engagement.