AI on your own hardware, from first question to running production.

Three services, one engagement

1. 01

   Advisory

   Find where AI actually pays off — and where it doesn't.

   We sit with your team, look at the real work, and separate the AI use cases that move a number from the ones that just sound good in a board deck. You leave with a roadmap scoped to your data, your constraints, and your hardware budget — not a vendor's product catalogue.

   What you get

   • A prioritised shortlist of use cases, each with the expected payoff and the honest effort to ship it
   • A hardware and model plan — what runs locally today, what needs a GPU, what to buy or rent
   • A delivery roadmap and a clear go / no-go on every idea, including the ones we tell you to drop

   Who it's for Teams who know AI matters but want a straight answer on what's worth building before they spend a krona on it.

2. 02

   Build

   Turn the roadmap into software that runs on your stack.

   We build the thing — the data pipeline, the retrieval layer, the model integration, the UI your people actually use — and we ship it into your environment, not ours. Local models where they fit, a documented fallback where they don't, and code your own engineers can read and own after we leave.

   What you get

   • Production software integrated with your systems, deployed inside your boundary
   • Local-first models with retrieval over your own documents — no data sent to a third-party API
   • Source, tests, and handover docs you keep, so you're never locked to us

   Who it's for Organisations with a validated use case who need it built right, on their infrastructure, by people who've done it before.

3. 03

   Deploy & Operate

   Run the local LLM and inference stack in production — on your infrastructure.

   We stand up and run the inference stack on your hardware: model serving, GPU scheduling, autoscaling, monitoring, and updates. You get a system that stays up and stays current without a third-party API in the loop. AtBice doesn't host public inference — this is for your infrastructure, kept inside your boundary.

   What you get

   • A deployed inference stack on your hardware — model serving, GPU scheduling, and autoscaling
   • Monitoring, logging, and alerting tuned for LLM workloads, with model and dependency updates handled
   • Runbooks and an on-call handover, or an ongoing operate agreement if you'd rather we keep running it

   Who it's for Teams running models in production who need it reliable and observable, without shipping their data to someone else's cloud.