Are you a final-year master’s student looking for a thesis project that blends real-world impact with cutting-edge tech?

At Framna, we are passionate about creating digital products that shape markets and make everyday life better - and we want you to be part of it.

Every year, we welcome a group of master’s students into our Stockholm studio to spend their final semester with us. You will sit alongside our developers, designers, testers, analysts, and product delivery leads, get a place to sit in our cozy office, and join our social and learning activities - from lunch-and-learns to our weekly Friday demos and AWs. It is a chance to do your thesis in a collaborative setting, surrounded by people who are happy to share what they know.

Background

Recommender systems play a central role in helping users navigate large product catalogs. Traditional methods often rely on metadata, collaborative filtering, or embeddings trained on structured product data. While effective, these approaches can struggle in domains where product descriptions are nuanced or subjective - such as wines, cosmetics, or books.

Generative AI offers a new paradigm: models can reason about product attributes in natural language, infer relationships, and generate explanations for why two products may be similar. This opens up opportunities for building recommender systems that are more intuitive, flexible, and user-centered.

This project investigates how generative AI can be applied to product similarity tasks, aiming to provide both methodological insights and a functional prototype.

What you will do

Some potential tasks within this thesis include:

• Collecting and preprocessing product data, including descriptions, attributes, and metadata. Augmenting data points and defining meaningful relationships between products
• Building and evaluating multiple approaches, including LLM-based methods and traditional recommender baselines
• Comparing model outputs through programmatic evaluation (e.g. LLM-as-a-judge), expert interviews, or benchmarking against existing recommendation systems
• Assessing performance with a focus on the relevancy and explainability of recommendations
• Developing a working proof-of-concept recommender system that integrates the chosen approach
• Summarizing best practices and recommendations for applying generative AI to product similarity tasks

Who you are

This thesis may suit you if you:

• Are interested in recommender systems and personalization
• Enjoy working with generative AI and data modeling
• Want to build a system that combines research with hands-on prototyping
• Plan to start your master’s thesis project in January 2026
• Have the opportunity to work from our Stockholm studio

About Framna

Framna means ‘to bring forward’ in Old Norse. And that is exactly what we do. We partner with industry leaders (and those about to be) to create excellent digital products that define markets, reshape industries, and deliver meaningful impact.

Born from the union of digital agencies Bontouch, Move, and Shape, Framna emerged from a strong, product-led culture. Today, over 600 of us collaborate across ten studios in Denmark, the Netherlands, Poland, Sweden, Switzerland, and the US.

Every day, millions of users around the world rely on the products we help shape. From seamless payments to daily commutes, from better health management to smarter shopping. Our work supports more than €150 million in daily transactions and consistently earns an average App Store rating of 4.5.

Together with some of the world’s most ambitious brands, including Essity, SJ, Swish, and SEB, we craft products that lead markets and push boundaries. Because when products win, businesses win. We call it Win by product.

Our hiring process

• Psychometric assessment - personality and logic test
• Interview with our recruiter, Josephine
• Case assignment
• Case interview