Private Model

GPT-4 knows a lot. It doesn’t 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 can’t send it to a public model — not because the models are bad, but because the moment data leaves your environment, you’ve lost control of it.

Private model enablement is the answer. We help you run AI against your internal data without it touching a public endpoint.

Private cloud managed services

AWS Bedrock, Azure OpenAI, Google Vertex AI — these give you access to frontier models running in your cloud tenancy. Your data stays within your cloud account. No third-party model provider receives it. This is the fastest path to private AI for most organisations and the right answer for most use cases.

Self-hosted open models

Llama 3, Mistral, Phi, Qwen — capable open-weight models that run on your own infrastructure. Full control, no API costs, no data ever leaves 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 — your documentation, your support tickets, your product knowledge — without that data leaving your environment. Fine-tuning produces a model that’s genuinely expert in your domain. More expensive to produce, but can dramatically 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 — not the one that’s most technically interesting.

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 runs in your cloud account — your data stays in your environment from day one.