Flight operations, tires, baggage, vehicle telemetry — the domain changes, the craft doesn’t. Bring us your domain; we bring the engineering.
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.
And we build the platform they run on. Sovereignty is not a matter of conviction — it is a cost calculation: licence costs that grow with your business. Dependency on one vendor’s roadmap and pricing. And regulation that wants to know where your data lives. Our answer is always the same: with you — in your own data centre, hybrid or in the cloud of your choice. Open source is our tool for that, not the goal.
Does this sound familiar?
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.
The cloud bill grows faster than the business — and every new environment, every new team is another pricing conversation.
The auditor asks where the data lives. The honest answer: with the cloud provider — outside your control. And nobody in the house can say what an exit would cost.
If more than two of these apply, a conversation is worth it.
Security is part of the package, not next to it: hardening, encrypted access, continuous security updates and assessments are part of the delivery — not an add-on. Our entire own IT runs on the same platform we build for you.
What we deliver.
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Decision services and operational APIsData products as machine-to-machine services or with a user interface that flow into live business processes — routing, scoring, validation and recommendation logic with seconds latency and an audit trail. The operational application layer on top of your lakehouse, not the next reporting layer.
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Machine learning and agentic AI in productionML models and agentic-AI workflows from lab into production — with reproducible rollout, versioned results, drift monitoring and SLA. Deployed where it helps your operation: agents with clear decision boundaries prepare decisions, surface anomalies and make processes explainable. AI augments your business logic — it doesn't replace it.
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A data foundation for operationsWe extend your lakehouse with the operational layer: integration of heterogeneous source systems, validated data paths, a shared data foundation for multiple use cases and teams. One platform instead of one tool per question.
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Private and hybrid cloud in your own data centreA complete platform on your own hardware — from bare server to productive operations, combined with cloud services where you want them. Identity, backups, monitoring and access control are included from the start. Operated by your IT or by us — both with defined response times.
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Away from the hyperscaler — without the riskWe build so you can leave any provider without starting over: cloud services only where they bring a clear advantage, data and applications in open formats. And if a hyperscaler is the right answer for your case, we say so.
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Open source with someone to callThe opposite of DIY risk: we take the vendor role for the stack we deliver — support, security updates, guaranteed response times, assessments up to penetration tests. Open code means security claims you can verify instead of having to trust.
Aktuelles──Data Products & Hybrid Cloud: Insights & Case Studies3 Beiträge
OpenScorecard evaluates partners, suppliers and business units across multiple levels — one platform, configurable scoring, self-service access for the evaluated side. Frameworks and questionnaires are configuration, not code. Apache 2.0 foundation, exit-ready, on-premise-capable.
When two corporations found a joint venture, the new entity needs its own IT foundation — on day one. Multi-tenant, data-sovereign, no vendor lock-in. We set it up: 100% open source, on-prem Kubernetes, several use cases on one base.
In practice, data scientists rarely work on production-near data. Sample exports, anonymized snapshots, stale states are the rule. MLflow on a shared operations data foundation shifts that.