You’d think global retailers know what’s on their shelves. After all, these are billion-dollar organizations with access to data, tools, and talent. But during our first years working at retailers and consulting firms, we kept seeing the same thing: Even the most “sophisticated” retail organizations struggled to answer basic questions like: Which products are driving margins? Which categories are bloated? How do we compare to our competitors? This disconnect was eye-opening.
The disconnect was striking. Strategic assortment decisions were often based on gut feeling or outdated dashboards rather than real performance data. Millions worth of inventory sat idle while pricing strategies lagged behind the market. This wasn’t just inefficient, it was a huge missed opportunity. All while global platforms like Amazon and Temu were setting new standards for speed, responsiveness, and data-driven precision.
That’s when the idea for Zenline was born: AI agents for category teams
And for this, it felt like we were the perfect match: Arber with a background in strategy and consulting, Gerrit a seasoned engineer with experience building AI systems. Years of friendship and joint projects at ETH Zurich created a foundation of trust, shared ambition, and speed. In a landscape where AI is moving fast, speed is everything.
Retail is changing. So are the rules
AI is no longer hype. Every industry is being reshaped by intelligent software, and retail is one of the few still playing catch-up. We believed that the ones who adopt first will lead the market. Our vision: A future where every retailer has AI agents that handle analysis, flag cannibalization effects, identify assortment gaps, and recommend actions – while humans focus on execution, negotiation, and decision-making. Category managers will evolve.
Our journey began with fast iteration
When we started building Zenline, we explored both sides of the market: manufacturers and retailers. Initially, we weren’t sure where the bigger pain point was. But after talking to dozens of category managers and validating assumptions through prototypes, one thing became clear: the real pull came from retailers. It’s where the problem was most urgent and where decision-makers were actively looking for tools that actually worked.
The first pilots came through warm intros, the next ones (with more confidence) from outbound. We listened closely, kept iterating, and quickly saw that the value was in giving category teams better assortment decisions.
Thanks to many, many, many 5am nights, and a lot of AI usage, we were able to build our first version within four weeks and went live.
And maybe the most telling moment came right at the start
Amidst all the late-night prototyping and fast feedback loops, there was one moment that stuck with us: A surprise launch party, thrown by a group of close friends – many of them engineers, investors, smart people – with one message: “We’re behind you. Even if you never answer our WhatsApp messages.” That’s when it clicked. We weren’t just solving a problem. We had people around us who believed in the vision and pushed us to go all in. That’s when we knew: this is real. Now it’s full speed ahead.
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