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    Service 07

    On-Premise & Private LLM Deployment

    When compliance, intellectual property or cost makes a public API a non-starter: a model sized to your hardware, tuned on your documents, wrapped in an agent that can use your internal tools, and documented so your own team runs it without us.

    Price
    from $8,000
    Time to live
    3–4 weeks
    On-Premise & Private LLM Deployment

    What it does

    • 01Model selection sized to your hardware budget, benchmarked on your tasks
    • 02Fine-tuning on your documents and SOPs where retrieval alone is not enough
    • 03Agent layer with safe, logged access to your databases and tools
    • 04No per-token bill: the cost is the hardware and the electricity
    • 05Data residency by construction: nothing leaves the network
    • 06Runbook: restart, update and swap models without a vendor on the line

    What ships in the first two weeks

    1. Week 1: hardware audit, model shortlist benchmarked on your evaluation set
    2. Weeks 2-3: model deployed, tuned and wired to the first internal tool
    3. Week 4: runbook, monitoring, handover session with your ops team

    Every deliverable ends with code in your repository, a runbook and three months of fixes. Scope and price are fixed in writing before we start.

    Questions

    01Is an open model good enough?

    For most document, support and internal-tool tasks, yes, once it is grounded in your data and evaluated on your questions. We benchmark the shortlist on your evaluation set before committing hardware, and we say so if a public API would be the better call.

    02What hardware do we need?

    It depends on the model size and the load. A single GPU server covers many mid-size teams; we size it from your expected requests per day rather than from a vendor's spec sheet.

    03How does this help with the EU AI Act and GDPR?

    Data never leaves your network, every request is logged on your side and there is no third-party processor to paper. It does not remove your obligations under the AI Act, but it removes the hardest ones about data transfers and sub-processors.

    04Who maintains it after launch?

    Your ops team, with the runbook we hand over: restart, update, swap models. We stay on the line for fixes for three months, and there is an ongoing program if you want us to keep iterating.

    Go deeperAI compliance and risk in 2026: what actually applies to you

    Scope this in a 30-minute call

    You describe the process, we tell you what a first version costs and how long it takes. Free, no slides.

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