Three ways to work with us. We build and run ML systems on infrastructure you control, we place MLOps and data engineers inside your team, and we run Egen, a finished company AI, for teams that need no build. Whichever you choose, you keep the source, the documentation, and the ability to run it without us.
We build the system and run it until your team can. Production builds on infrastructure you control, ML Ops on a retainer designed to shrink, and private AI set up on your own hardware.
Read more →A Quince engineer inside your team, by the hour, by the month, or for a fixed scope. Pipelines, feature stores, model serving, monitoring, and the migration off providers you cannot afford to depend on.
Read more →When you need no build at all. Our company AI for documents, meetings and your own systems, run in Sweden for a flat monthly price.
Read more →Two stages, each fixed in scope, each ending in something you keep. Stop after either and walk away with the deliverables.
A Quince AI Production build turns your use case into a running system on infrastructure you control, on premises or in a sovereign EU cloud, handed over with full source, documentation and monitoring.
Your use case becomes a system. We build the pipelines, harden the serving layer, and put the models into production where your data already lives (on-prem, private cloud, or at the edge), with monitoring, rollback, and reproducibility from day one.
ML Ops & improvement is a monthly retainer covering monitoring, retraining and team enablement, designed to end when your own engineers no longer need us.
Models drift, data changes, regulation moves. We monitor, retrain, and improve the system, while deliberately training your team to take it over. The retainer is designed to shrink: success is your engineers not needing us on the phone.
Not every organisation needs a model built from scratch. Most need the tool everyone already knows how to use, running somewhere their material is allowed to go. We set that up: an open-weight model, an interface your team can use on day one, and your own documents behind it. On hardware you own, on a device, or in a Swedish data centre.
We'd rather understand two industries properly than ten superficially, and both of ours run on trust, jurisdiction, and uptime.
Visual inspection, predictive maintenance, energy optimisation: machine learning that runs on the factory floor, OT-safe, with no round-trip to anyone's cloud.
Machine learning for organisations that answer to citizens: data residency in Sweden, GDPR and EU AI Act compliance designed in from the first commit, and systems your own staff operate.
We map every AI system on five rungs of control, from Exposed to Owned. The self-check tells you which rung you stand on today. The work above is the climb.
Tell us the problem and the constraints. We'll say whether machine learning fits, which of the three ways suits it, and what it would take to run it on infrastructure you own.