Private AI Model Deployment

A public model knows a lot. It does not know your customers, your contracts, your internal processes, your pricing, or anything else that makes your business what it is. That knowledge lives in your systems. And for most of it, you cannot send it to a public model, not because the models are bad, but because once data leaves your environment you have less control over it.

Private model enablement is the answer: running AI against your internal data without it touching a public endpoint.

Private cloud managed services

AWS Bedrock gives you access to frontier models running in your own AWS account, and your data stays within that account. This is the fastest path to private AI for most organisations and the right answer for most use cases.

Self-hosted open models

Capable open-weight models, such as the Llama, Mistral, Phi and Qwen families, run on your own infrastructure. You get full control and no per-request API costs, and your data stays on your servers. The trade-off is that you’re responsible for the infrastructure, scaling, and keeping the model updated. Right for organisations with high query volumes, strict data requirements, or specific compliance needs.

Fine-tuning on internal data

A base model trained on your proprietary data, such as your documentation, support tickets and product knowledge, without that data leaving your environment. Fine-tuning produces a model that is genuinely expert in your domain. It costs more to produce but can outperform RAG for the right use cases. We help you decide when it’s worth it.

Private RAG against internal documents

The simplest pattern: a retrieval pipeline over your internal documents, running against a model in your cloud environment. No fine-tuning needed. Documents stay in your infrastructure. Updates happen as your documents change. Right for knowledge bases, internal Q&A, document search, and support automation where the answer already exists in documents you have.

Choosing the right pattern

The decision depends on factors that aren’t obvious: how often the underlying data changes, whether you need the model to reason across information or just retrieve it, what your regulatory requirements are, and what query volume looks like at steady state. We assess your use case first and recommend the approach that fits.

What we deliver

Use case assessment and architecture recommendation. Infrastructure design for your chosen deployment pattern. Embedding pipeline and vector store for RAG-based approaches. Model deployment and API layer for self-hosted approaches. Integration with your existing data platform and governance controls. Everything can run in your own cloud account when your data must stay within your environment.

Want to learn more? The forward deployed engineer case study shows local models doing the sensitive step of a real production line, with the data never leaving the machine. Why every enterprise AI product needs a professional data platform covers the data side that has to be in place first.