Reliable data foundations for people and AI

When we started working with Mixit, data was spread across separate systems, making it harder to work with consistently.

We built a centralized data warehouse in BigQuery, connected Mixit's data sources and created reusable data models. This reduces the need for additional data processing in dashboards and individual analyses.

Building from scratch gave us the opportunity to design the architecture properly from the outset.

A reliable data warehouse is only part of the solution. AI agents also need to understand what the data means, which metrics to use and how information should be interpreted.

That's why we added a semantic layer, integrated a data catalog and prepared guidance for AI agents. This gives people and AI systems a shared foundation for working with the company's data, while reducing unnecessary queries and processing costs.

Read more about preparing data for AI agents.

Our collaboration with Mixit is ongoing. Alongside further development of its data infrastructure, we support marketing measurement, reporting and attribution.