Private AI for enterprises that can’t send data elsewhere
For a lot of organisations the barrier to AI adoption has nothing to do with capability and everything to do with where the data goes. Sending customer records, clinical data or commercial contracts to a third-party API sits badly with regulators, clients and often the board. We build private AI systems that run inside your environment or on European infrastructure you control, using open-weight models that give you capability without the dependency.
Our private AI services
We cover architecture, deployment and ongoing operation of AI systems that keep your data under your control.
Private AI platform deployment
We deploy a complete private AI platform inside your environment, giving your teams access to models, retrieval over internal data and application tooling without anything leaving your infrastructure.
On-premise and EU-hosted infrastructure
We design and deploy the underlying infrastructure, whether that’s on-premise hardware, private cloud or European-hosted infrastructure chosen for jurisdictional reasons rather than convenience.
Open-weight model selection and tuning
We assess the open-weight model landscape against your specific requirements, and handle the fine-tuning or adaptation needed to close the gap between a general model and your domain.
Secure retrieval over internal data
We build retrieval systems that let AI answer from your own documents, knowledge bases and systems, with access controls that respect the permissions your organisation already has in place.
Sovereignty and compliance architecture
We design for the regulatory position you’re actually in, taking account of GDPR, the EU AI Act, sector requirements and the extraterritorial reach of non-European legislation such as the US CLOUD Act.
Managed private AI operations
We can run the platform for you, covering model updates, performance monitoring, cost management and the ongoing engineering that keeps a private AI estate current.
Private AI technologies and infrastructure
We build on open-weight models and infrastructure you can genuinely control, selected against your residency requirements and your expected workload.
Private AI cases
We work with organisations in healthcare, logistics, financial services and the public sector where data residency and control determine what’s possible.
Our private AI process
Establishing your constraints
We start with the requirements that actually bind you, covering data residency, regulatory obligations, client contracts and internal security policy, because those shape every decision that follows.
Designing the architecture
We design the deployment model, covering where the infrastructure sits, which models it runs, how data flows through it and what the cost profile looks like at your expected volume.
Deploying the platform
We stand up the infrastructure and platform, integrate it with your identity and access management, and connect it to the internal data sources your teams need.
Building the use cases
A platform on its own changes nothing, so we build the first applications alongside your teams and establish the patterns for everything that follows.
Operating and evolving
We monitor performance and cost, update models as better open-weight options arrive, and keep the platform aligned with a regulatory environment that continues to move.
Enabling users to be part of the design and delivery process in such an artful way makes the leaders of this business quite unique.
The WeAreBrain team stood out because they understood the problem we were trying to solve, and how we aimed to solve it.
Their speed and their attitude were impressive — the speed of their work is limited by your speed only!
FAQs
Questions about deploying AI privately? Find the answers here.
What is private AI?
Private AI describes AI systems that run on infrastructure the organisation controls rather than through a third-party API. Your data stays inside your environment throughout, which removes an entire category of compliance and confidentiality questions.
Is private AI as capable as commercial AI services?
For most enterprise use cases, yes. The gap between leading open-weight models and commercial services has narrowed considerably, and grounding a capable open model in your own data usually matters more to output quality than the raw model benchmark does.
What makes a good private AI platform for enterprise?
The things that matter most are model flexibility, so you’re not locked to one provider, integration with your existing identity and access management, retrieval that respects internal permissions, and observability detailed enough to satisfy an audit.
Where does private AI infrastructure sit?
Wherever your requirements dictate. That might be on-premise hardware, private cloud, or European-hosted infrastructure under EU jurisdiction. We help you assess which option fits your obligations and your budget.
Why does jurisdiction matter if the provider is compliant?
Because compliance and jurisdiction are separate questions. Legislation such as the US CLOUD Act can reach data held by American providers regardless of where the servers physically sit, which is why some organisations choose European infrastructure and European providers specifically.
What does private AI cost compared to commercial services?
The cost profile is different rather than simply higher. Private deployment carries more fixed cost and less per-query cost, so it tends to become more favourable at volume. We model this against your expected usage before you commit.
