Ownership in practice
Concrete examples of what we've built and operated for our customers - over time, not in a single project. That's why most of our customers appear with several cases below.
Chat with your data
Hobbii is one of the world's largest online yarn stores. Data from ERP, Shopify, a web tracker and a long list of other sources sat scattered, unreliable - and always one IT ticket away from an answer. We've consolidated all of it into one foundation, exposed it in a data clean room and put a semantic layer on top with Cube. That's not a technical detail - it's the whole point: revenue, customers and the other key terms mean the same thing, every time, so the AI can only answer within the definitions and data set in the semantic layer, instead of guessing or inventing them. The data environment is closed - data never leaves Hobbii's own systems along the way.
Via agentic analytics, Hobbii's senior staff now talk directly to their own business data in plain language and get answers and visualizations through Metabase, inside Claude, in seconds. A dashboard looks backward. This is the opposite: agents that come with reflections based on facts - not a static snapshot someone has to remember to open.
- Hobbii has said goodbye to their previous dashboard tool, Locker, and saves over 30,000 euros a year
- The data team is no longer a bottleneck - the business finds its own answers, from question to insight in seconds instead of days
- Decisions get made faster, on data everyone can trust
- Ongoing improvements that build on the foundation we've laid together
„Usernest is the pragmatic partner for e-commerce businesses looking to get their data setup right. Their insights into composable architecture and strategic guidance show the way forward based on deep understanding of the key data use cases.“
- Tomas Antvorskov Krag, CTO, Hobbii
One member, the whole way round
CA a-kasse's challenge is one we recognize from many of the organizations we talk with regularly: data about the same member sat fragmented and siloed across different systems, with no way to recognize it was one and the same person across channels and touchpoints. We've consolidated all data into one data lake and done identity stitching, so a member can now be recognized wherever they show up in the system landscape - and CA can, for the first time, see the whole member journey as one.
It sounds like a technical detail, but it's actually the cornerstone everything else gets built on. Without one recognizable member, there's no coherent insight, no reliable segmentation, and no way to act proactively on a member's situation. With identity stitching in place, CA now has a foundation where every touchpoint - a call, an application, a course, a job posting - can be linked to the same person and together tell the story of that member's journey.
- All data consolidated into one data lake, across previously siloed systems
- Identity stitching means the same member is recognized across channels and touchpoints
- The whole member journey can now be seen as one - not as fragments spread across different systems
- The foundation for acting proactively on a member's situation, instead of reacting after the fact
From customer list to customer radar
Wexøe Industry distributes cable joints, connection technology and electrical accessories to installers and utility companies across Denmark - thousands of customer relationships that used to live scattered across order history, spreadsheets and individual salespeople's memory. On top of Core, we've built an RFM model that continuously scores every customer on how recently, how often and how much they buy. The result isn't a static spreadsheet - it's a living picture of the whole customer portfolio, updated as orders come in.
Sales can now see immediately which customers are quietly slipping away, who's become more important than last quarter, and where their time is best spent - instead of prioritizing on gut feeling. Wexøe runs Core with Operate: we build and run the foundation, they own the data and the direction.
The next phase is agentic prompting. Instead of key employees having to remember to ask for or go looking for insights, they get them delivered proactively, wherever it makes a difference - Finance, Sales, Warehouse and the other parts of the organization where an early signal is worth acting on.
- RFM segmentation of the entire customer portfolio, continuously updated on top of Core
- Sales sees who's slipping away - before it hits the bottom line
- Wexøe runs Core with Operate: we run the stack, they own the data
- Next phase: agentic prompting rolling out to key employees in Finance, Sales and Warehouse
Does one of the cases look like your situation?
Book a call, and let's talk specifically about your data and your use case.