Production machine learning you end up owning.
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 build products on our own edge stack. Whichever you choose, you keep the source, the documentation, and the ability to run it without us.
Services
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 →MLOps & Data Engineering Consultants
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 →Products
What we build on our own stack. MQ Flow, an AI system for heating and ventilation built with our partner Mifimi, and Qonverge, our talent matching platform.
Read more →We build it.
You end up running it.
Two stages, each fixed in scope, each ending in something you keep. Stop after either and walk away with the deliverables.
Production build
Scoped per projectA 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.
You receive
- A running system on infrastructure you control, integrated with your stack.
- Full source code, documentation, and reproducible training pipelines.
- A handover your team can run: runbook, training, and no dependency on Quince.
How it works
- Fixed-scope milestones with demos every two weeks. Never a black box.
- Portable by construction: standard interfaces, no provider you can't replace.
- OT-safe delivery for factory environments; procurement-ready for public sector.
ML Ops & improvement
Monthly retainerML 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.
You receive
- Drift, cost, and latency monitoring with agreed response times.
- Scheduled retraining and evaluation, with every change documented.
- An operations runbook and hands-on team enablement.
How it works
- Monthly, cancellable: the relationship is a choice, not a dependency.
- Every intervention is documented so your team learns from it.
- Scope reviewed quarterly, and reduced as your team takes over.
Run a private assistant
on your own hardware.
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.
Every stage moves you
up the ladder.
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.
Thirty minutes to an honest answer.
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.