AI deployment on infrastructure you control
A first production use case, built to pass audit.
Overview
Lindstead takes a first AI use case to production, on-premise or in a European cloud, with the controls auditors expect. The organisation's own team then runs it.
- ResultA defined use case taken to production, with access, logging and evaluation.
- InputThe use case, data constraints, existing infrastructure and acceptance criteria.
Key questions
- How do we get from pilot to a production AI system we control?
- Which controls will our auditors expect?
- Can AI stay in our data centres and still use strong models?
- Can our own team run it after go-live?
Approach
- Architecture Model serving, retrieval, access control and logging, hosted where the data must stay.
- Pilot deployment One use case live with real users, measured against agreed quality criteria.
- Controls and handover Security testing, evaluation, monitoring and documentation, then handover.
Deliverables
- Production use case One AI use case live on the organisation's own infrastructure.
- Security and quality controls Access control, logging, evaluation and monitoring, documented for audit.
- Team handover Runbooks and training for the organisation's own team.
Frequently asked questions
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A large language model on infrastructure the organisation controls, on-premise or in a dedicated cloud, instead of an external API.
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Yes, if the licence allows it and security, logging and access controls are in place. Self-hosting removes third-party access and data transfers, not prompt injection or over-broad permissions.
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Scaleway, OVHcloud, Nebius, Verda and Hetzner, among others. Ownership and jurisdiction differ. Lindstead's GPU price tracker lists each with current prices.
Related
Discuss deployment with Lindstead.
A 30-minute meeting covers the use case, the requirements and a first plan.