Google Cloud is much more than just infrastructure. With BigQuery for scalable data analysis, Vertex AI for machine learning and AI workloads, Looker and Data Studio for business intelligence, and a broad ecosystem of managed services, it provides a powerful platform foundation for modern data architectures and AI applications.
Together with you, we develop a Google Cloud solution that fits your data landscape, your processes, and your level of maturity. Whether it's building a modern data platform, migrating existing workloads, or implementing new analytics and AI scenarios—we bring strategy, architecture, and execution together to turn potential into productive operations.
Whether you want to evaluate GCP, modernize your existing data landscape, or scale a successful pilot, we support you exactly where you are today.
A new data or AI initiative is on the horizon, but the platform, architecture, and approach have not yet been determined. We assess your requirements, existing systems, and planned applications to provide a well-founded recommendation.
Your current data warehouse or cloud setup is difficult or costly to expand and maintain. We evaluate which components can be retained and develop a step-by-step migration plan to Google Cloud.
You have initial ideas or pilot projects, but the data foundation, integration, or operating model are not yet fully defined. We establish the technical prerequisites and implement your prioritized use case in a production-ready environment.
Your Google Cloud environment is growing, but access, responsibilities, monitoring, and cost control are not consistently managed. We develop appropriate standards and integrate them into your architecture and operations.
We transform individual use cases into a scalable platform, taking into account governance, workspace structures, deployment processes, monitoring, and capacity management.
Together, we define which GCP services fit your requirements and design a scalable, cost-efficient target architecture.
We design and implement modern data platforms using BigQuery, Cloud Storage, Dataflow, Dataform, and other suitable Google Cloud services.
Using Vertex AI and the Gemini Enterprise Agent Platform, we develop production-ready AI applications for analysis, automation, and agentic workflows within your company.
We help you not just store data, but make it actionable. To do this, we develop dashboards and self-service analytics solutions for your business departments.
We support you in migrating existing data platforms, BI solutions, pipelines, or applications to the Google Cloud.
We ensure transparent and controllable GCP usage. We set up Dataplex, IAM, and monitoring to keep your GCP environment secure and traceable.
We are not just an implementation partner that sets things up and disappears. We focus on business value from the very beginning and stay with you until your platform is truly up and running.
We start with a concrete use case, deliver initial results early, and continue to expand the platform iteratively.
Our consulting doesn't end with the target vision. We implement architectures, pipelines, data models, dashboards, and AI solutions that are ready for production.
taod brings together experience in data engineering, BI, data science, and AI. This creates not just an isolated cloud infrastructure, but a platform that moves both business departments and IT forward.
We think strategically across the board. This means we work with you to identify which measures will have the greatest impact.
Our goal is not to create dependencies. We document, explain, and empower your teams so that your organization can securely use and further develop the platform in the long term.
With over 650 successful data and AI projects, we have the experience and expertise to support you in your endeavor.
We have over 55 certified data experts who are continuously expanding their skills.
Our team includes data engineers, data analysts, and AI engineers. This ensures we can support projects throughout the entire data journey.
We analyze your current situation, goals, and priorities. This quickly clarifies where Google Cloud offers the most leverage and how best to get started.
Based on your requirements, we design a GCP architecture that fits your stack.
We implement the solution on GCP, integrate relevant systems, and build the foundation for stable, high-performance, and usable applications.
We provide ongoing support if desired, including new use cases, AI features, and continuous optimization.
GCP is particularly strong when analytics and AI are at the core. BigQuery is one of the most powerful analytics services on the market, and Vertex AI offers a mature platform for ML workloads. Companies that already use Google Workspace or are looking for an open cloud platform without strong vendor lock-in are often well-served here. We will determine whether GCP is the best choice for you during our initial consultation.
In many cases, yes. BigQuery can completely replace traditional on-premise warehouses while offering significantly more flexibility in terms of scaling and costs. Whether a full migration makes sense depends on your existing landscape, your BI tools, and your latency requirements. We will analyze your situation and recommend a suitable target vision.
Yes. GCP integrates well into existing multi-cloud environments. If you are already using Azure or AWS, for example, and want to use GCP for specific workloads, we can support you from the architectural decision-making process through to technical implementation.
No. Google Cloud can also be a great choice for modern application architectures, integration scenarios, and scalable operating models. GCP is particularly strong where data, analytics, and intelligent applications need to work together.
By establishing a clear target vision, defined responsibilities, and an architecture that isn't just designed for the initial use case. That is exactly why we look beyond technical implementation to include operations, governance, and ongoing development.
We're taking your data management to a new level. Strategically and technologically.
From building a new modern data warehouse in Azure to customer-centric data management
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