Landskrona-based Noorlytics started with a problem Aisha Kujović kept running into. Many organisations want to use AI, but things become much more complicated when sensitive data, internal systems, and real business responsibility are involved. Who can access the data? Where does it go? How much control do you actually have? And who is responsible if something goes wrong?
The answer Noorlytics gives is simple in principle: instead of bringing sensitive data to AI, bring AI to where the data already is.
The insight came from hands-on experience. Working with a large and sensitive codebase, Aisha Kujović wanted to use AI to understand and upgrade it, but was limited to not sending sensitive code or information outside the organisation.
“I identified the problem while working with a large and sensitive codebase. I wanted to use AI to help me understand and upgrade it at scale, without sending sensitive code or internal information outside the organisation. So I built my own platform that uses an open AI model on my own machine.”
Aisha Kujović said
Now Noorlytics has been selected for the Øredev Emerging Tech Program 2026 and founder Aisha Kujović will present the work in Malmö this autumn.
Noorlytics assesses code health and connects technical findings to business risk, flagging vulnerabilities, outdated dependencies, or technical debt that could create security, operational, or compliance risks for the organisation. The bigger idea is that AI should help organisations make better decisions about their technology, without forcing them to give up control of sensitive data in the process.
The platform has already been built and tested, and Kujović is keeping it open source at this stage. She wants people to be able to try it, challenge it, and help identify where it creates the most value, letting the technology prove itself before building a commercial layer around it, similar to how companies such as Neo4j had strong open-source roots early on.
Over the next year, the focus is to move Noorlytics into real organisational use, work with companies that have complex software environments and sensitive information, and turn the strongest use cases into pilots and eventually a commercial product.
