Your data stays yours. Your AI runs on your terms.

For most family office use cases — document analysis, meeting summarisation, research synthesis, structured reporting — modern open-source models perform at or very close to frontier model quality. In some specialised tasks, they can be fine-tuned on your data to significantly outperform general-purpose commercial models. The gap that remains is most pronounced for highly complex, open-ended reasoning tasks, where a governed outbound gateway to frontier models covers the shortfall.

With on-premises deployment, your data never leaves your physical infrastructure. With private hosted cloud, your data sits in a dedicated environment with no shared tenancy, encrypted at rest and in transit, accessible only through private network endpoints. Simple operates as a processor under your data governance policy, not as a data controller. You retain full ownership and audit rights.

Data residency requirements, GDPR and equivalent privacy regimes, and fiduciary obligations all constrain how family office data can be processed. Sovereign AI satisfies data residency requirements by keeping data within defined geographic boundaries. It provides a complete audit trail for regulatory reporting. And it removes the risk of sensitive client or portfolio data appearing in a third-party model's training pipeline — a growing concern with closed commercial providers.

The honest answer is that sovereign AI requires meaningful upfront investment — in hardware or cloud infrastructure, in deployment and configuration, and in ongoing maintenance. For offices running high volumes of AI workloads, the total cost of ownership over three to five years is typically competitive with or lower than commercial API costs. For smaller offices, private hosted cloud models significantly reduce the infrastructure barrier. We provide a detailed cost modelling exercise as part of every initial assessment.

The open-source ecosystem is specifically designed to avoid lock-in. Because your infrastructure, your knowledge base, and your agent workflows are all built on open standards, changing the underlying LLM is a configuration decision, not a migration project. New models are released regularly — Gemma, Llama, and Qwen all release updated versions several times per year — and upgrading is a managed operation, not a vendor negotiation.

A private hosted cloud deployment can be operational within four to eight weeks from initial assessment to production. On-premises deployments depend on hardware procurement and physical installation, typically taking eight to sixteen weeks. Both timelines include model selection, data pipeline configuration, agent workflow design, governance framework setup, and staff training. Simple manages the full deployment process.