Decision services, ML and data foundation — in production in your business.

Data products only work where they don’t stay in the notebook. We bring models, decision services and shared data foundations into live business operations — with SLA capability, reproducible rollout, versioned results and an audit trail that makes statements traceable.

Does this sound familiar?

• Your data scientists work on sample exports that arrive once per quarter from the data-warehouse team. Edge cases are missing in the sample. Drift stays invisible until deployment.
• An ML model or agentic-AI workflow is ready in the notebook — the path to a productive service with SLA takes six months.
• You want to put a decision service into production (e.g. for dispatch, pricing, risk scoring) — today your team builds that as custom code per use case, without a shared platform template.
• The lakehouse delivers reports — the operational application layer on top (API, UI, ML service, audit trail) gets rebuilt every time, because the foundation is missing.

If more than two of these apply, a conversation is worth it.

Cloud-agnostic by default, integrated into your existing software landscape, open source as the foundation — exit-ready, on-premise-capable, no vendor veto over your data strategy.