Many companies test AI on systems that were never built for it. The pilots run, the scaling fails. On compute, on interfaces, on cost. With our partner Google Cloud we build the infrastructure on which AI and your system landscape can grow together. We know it works. Our own systems run on it, as do the system landscapes of several of our clients.
The prototype ran on a laptop, production needs scaling, monitoring and governance. With Vertex AI the experiment becomes a service: models, agents and pipelines on one platform that operations can actually carry.
ERP here, shop there, Excel and nightly jobs in between. We untangle it with APIs and services on Cloud Run: small, maintainable building blocks instead of one more monolith beside it.
Without a data foundation every AI stays blind. BigQuery brings the sources together and makes them queryable, for reports as much as for models. It's the same lesson as PIM and MDM, one layer deeper.
Fair question. German regions, EU data boundaries with Data Boundary and, on request, sovereign operating models up to air-gapped. We design the architecture so compliance is a property, not an afterthought.
Do the math first, then migrate. Three questions that make the business case for the cloud honest.
Your own servers cost around the clock, at night and on weekends too. Serverless flips that: you pay when something runs. For fluctuating loads and AI workloads that's often half the business case.
Less than you think, if you build it right. Containers, open standards and clean interfaces keep you agile. We'll tell you honestly where lock-in is real and where it's only used as an argument.
No. We start with one workload that hurts: a data problem, an AI use case, a system at its limit. After that you decide based on results, not on slides.
Google Cloud PlatformEvery department has its data, but no one has the full picture. Sales looks at the CRM, logistics at the ERP, marketing at five different tools — and every cross-functional question means someone spends days building a spreadsheet. According to Gartner, only 12 percent of companies have data of sufficient quality for AI applications. BigQuery on Google Cloud addresses exactly this problem: a central, maintenance-free data foundation that answers reports in seconds and feeds AI models from the same data.
Read more →~ 7 min
google-cloud-platformTraditional infrastructure is sized for peak demand — and costs the same every day as it does on that peak day. Serverless flips this model: you pay for what runs, and what doesn't run costs nothing. Cloud Run on Google Cloud makes this principle practical for everyday applications, without requiring teams to reinvent how they work. Here's how the cost model works, where its limits lie, and why we're writing about this from our own experience.
Read more →~ 8 min
Google Cloud PlatformThe most common question in German cloud projects isn't a technical one — it's a legal one: are we allowed to store our data there? Over the past few years, Google has built a remarkably concrete answer. €5.5 billion is flowing into German data centers and sites through 2029, Munich is home to the first Sovereign Cloud Hub, and the sovereignty portfolio ranges from EU data boundaries to fully air-gapped environments. Here's what you actually need — and how to answer the question properly.
Read more →~ 8 minYour problem: the machine data exists, but nobody can use it. The lever: a data platform plus AI for maintenance forecasts and quality, on infrastructure that scales with the sensors.
Your problem: peaks bring systems down, data analyses take days. The lever: autoscaling for load spikes and BigQuery for decisions in minutes instead of days.
Your problem: cloud yes, but compliance first. The lever: EU data boundaries, German locations and sovereign operating models, together with partners like T-Systems.
AI agents that take on tasks on their own need more than a model: clean data, clear interfaces, dependable operations. That's exactly what we build. First the foundation, then the use cases, then the scaling.
In 30 minutes we look at your system landscape: where it jams, what it costs, what the first workload in the cloud would be. Afterwards you have an honest assessment instead of a slide battle.