Area ── DATA PRODUCTS

Data that acts — services, predictions, decisions.

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.

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.

What we deliver.
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    Decision services and operational APIs Data 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 production ML 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 operations We 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.
Aktuelles ── Data Products: Insights & Case Studies 2 Beiträge
OpenScorecard — scoring for partners and business units
Data Products ── 2026-05-14

OpenScorecard — scoring for partners and business units

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.

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Data science in production
Data Products ── 2026-04-22

Data science in production

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.

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