Versioning, deployment, monitoring and retraining — so a model doesn't get stuck as a prototype. Fully within your own infrastructure if you want.
A model in a notebook is not a product. It lacks what every other piece of software takes for granted: versioning, a path to production, monitoring and someone accountable. This is exactly where most AI initiatives fail — not on the model, but on operations.
Four steps that form a circle. Only once the circle closes does a model stay useful over time.
Model, training data, hyperparameters and code are versioned together. Every result is reproducible — in a year's time and by someone else.
Models go to production through the same pipeline as your applications: tested, approved, reversible. As an API, a batch job or embedded directly.
Not just latency and error rate, but the quality of predictions too. Drift is detected before anyone in the business notices results getting worse.
New data flows into a new model that competes against the old one. Only the better one goes live — automated, without a gut call.
If your data must not leave the building, the AI runs at your site instead. Technically that is no longer a compromise.
Open models run on your GPUs — in your own data centre or in the Swiss cloud. No prompt, no document and no customer record leaves your environment.
The model only sees what the person asking is allowed to see. Permissions from your directory apply to the AI as well — not retroactively.
Every request, answer and source logged. You can prove afterwards how a decision came about — a requirement in regulated areas.
Your own hardware instead of per-token billing. With steady load that is predictable — and beyond a certain volume clearly cheaper.
One central registry for all models with state, metrics and approval status. Training and deployment pipelines as code, not manual work.
Model serving on Kubernetes with GPU sharing and autoscaling. Expensive cards stay utilised instead of belonging to individual teams.
Automated tests for quality, hallucinations and unwanted output — on every model change, before it goes live.
instead of "it worked back then" — every result can be rebuilt.
your data stays, if that is what you need — without giving up AI.
model lock-in: open formats and standards, you can switch provider.
Chosen vendor-neutrally, to fit the problem.
30-min intro call — free.
No standard pitch. I listen, ask the right questions and give an honest assessment — even if we are not the right partner.
