Berlin IT · Services
Software and Data Engineering
We build scalable applications and data pipelines.
Our engineering team transforms complex business needs into scalable, secure and high-performing digital solutions. We combine deep data expertise — Google Cloud certified Professional Data Engineers with hands-on pipeline design across GCP and SAP products, including BigQuery, Dataproc, Cloud Storage, Pub/Sub and Dataflow — with strong cloud-native application delivery in Azure, .NET Core, React.js and enterprise integrations using Azure Service Bus, APIs and Logic Apps. From streaming ETL pipelines built with Apache Beam, Python and Node.js, to PySpark and Databricks workloads, we embed solution governance, architecture design and stakeholder collaboration into every engagement. We are passionate about AI innovation, cloud-native design and DevOps automation that delivers measurable business impact and operational excellence.
Overview
Why teams engage us
Advanced analytics only pays off when the pipeline behind it is production-grade. We combine data engineering, data science and analytics translation so the insight actually reaches the decision.
For data, analytics and digital leaders who want measurable outcomes from advanced analytics — not another proof-of-concept in a slide deck.
Outcomes
What you can expect
- Reliable data pipelines feeding a governed analytics layer.
- Predictive and forecasting models validated on your real data.
- Analytics products embedded in the workflow, not stuck in a notebook.
- A pragmatic MLOps footprint you can maintain without a specialist team.
How we work
Our approach
- 01
Use-case shaping
We prioritise use cases by business value and feasibility, so the first model in production earns the budget for the second.
- 02
Engineering & modelling
Data engineers stand up the pipelines; data scientists build, validate and tune the models on the platform your team can operate.
- 03
Productionisation
Deployment, monitoring and drift detection — with runbooks so operations can own the model on day one.
Do you focus on Azure, AWS or on-premises?
We work across all three. Most of our recent work uses Azure Data Services and AWS analytics stacks, and we still support mature on-premises estates.
Can you deliver a proof of value in a short timeframe?
Yes. A typical proof of value runs 6–10 weeks with a clear success metric agreed up front. We only recommend production build-out once value is demonstrable on your data.
Do you offer generative AI and LLM work?
Yes, where it fits the business case. We focus on retrieval-grounded, evaluable solutions with guardrails — not novelty demos.
Who owns the models after go-live?
Your team does. We build with maintainability in mind, transfer knowledge as part of delivery, and offer support and management afterwards if needed.