Private language models
An AI workspace on your own servers.
Deploy locally hosted models and a staff interface on university infrastructure. Size the model and hardware around your workload, budget and access requirements.
Private AI & agents
Your university’s knowledge.
On your university’s terms.
Private language models, document assistants and AI agents, deployed on your university’s own servers. Useful tools for staff, with data control built into the plan.
A small London studio. Working directly with you.
What we can build together
Useful AI.
A defined boundary.
Start with one valuable use case, prove it on approved data and expand deliberately. We work with your IT and information security teams throughout.
Document assistants
Find answers in approved sources.
Connect selected policies, handbooks or project documents. Show source references and apply access controls across retrieval, so answers can be checked against the original material.
AI agents & workflows
Useful actions. Explicit permissions.
Build assistants for defined tasks such as preparing reports or drafting responses. Limit tool access, record actions and require human approval for consequential changes.
Operations & evaluation
A pilot you can assess. A service you can run.
Test answer quality and misuse scenarios with your team. Agree monitoring, updates, incident handling and support before making the service more widely available.
Designed for your institution
Private means the whole data path.
We scope models, document processing, search indexes, logs and backups to stay within your approved university environment. External APIs, telemetry and agent connections are reviewed explicitly; outbound network restrictions can enforce an agreed boundary. Security controls are designed and tested with your IT team for your data and use case.
A little clarity
Before we begin.
A few practical questions about private ai. We’ll work through the details with your team.
Can the system run entirely on our servers?
Yes, subject to suitable infrastructure and an agreed design. Local inference, document processing and storage can run on your university’s servers. Any identity service or external integration is included in the data-flow review.
Will our data be sent to a public AI provider?
For a fully local deployment, we configure local models and processing rather than public AI endpoints. We review every enabled integration and use network controls to restrict outbound connections to the agreed design.
What hardware do we need?
That depends on the model, document volumes, response time and number of concurrent users. We assess your existing servers and propose a pilot before you commit to additional infrastructure.
Can an agent act without staff approval?
Permissions and approval points are agreed for each workflow. We favour limited access and human approval for consequential actions, with records that let your team review what happened. AI outputs still need appropriate human checking.
Start a conversation
Make AI useful.
Keep your institution in control.
Tell us what you’re planning, what’s working and what isn’t. We’ll work out the next step together.
A 30-minute conversation. A clearer way forward.
Connected expertise