Product discovery has become one of the biggest friction points in e-commerce. Shoppers scroll through endless listings yet often see the same items repeated. Sellers upload quality products that rarely surface in search results. As online and in-store shopping continue to blend, the pressure on digital marketplaces to deliver accurate, real-time recommendations is rising fast.
Recent data shows that 74% of Spaniards will combine physical and online purchases in 2024, signaling that shoppers expect convenience, speed, and relevance across every channel. However, many platforms still rely on recommendation systems built more than a decade ago. The result? Static suggestions that fail to reflect what users actually want in the moment.
A Zurich-based start-up, Albatross, is attempting to change that dynamic.
A Strategic Move With Wallapop
Albatross recently secured its first major commercial agreement through a strategic partnership with Wallapop, one of Spain’s leading online marketplaces. Early trials produced measurable gains:
– 119% increase in user engagement
– 105% rise in favorites and interactions
– 47% growth in purchase intentions
These figures suggest that buyers interacted more frequently and showed stronger intent to purchase when the new discovery engine was active.
Rob Cassedy, CEO of Wallapop, stated:
“We are moving toward a system that understands what users want in real time, helping buyers find the right items faster while giving sellers more effective visibility for their listings.”
That focus on real-time understanding sets the foundation for Albatross’ approach.
Why Traditional Discovery Falls Short
Most e-commerce search engines rely on historical data to generate results. They study a shopper’s past activity, highlight products that are currently popular or trending, and compare behavior with that of similar users to shape recommendations.
Kevin Kahn, CEO of Albatross, argues that this backward-looking model limits relevance.
“We solve one major problem – the fact that conventional approaches to product discovery produce the same static recommendations over and over again.”
He adds:
“The underlying problem is that these strategies all look backwards. They’re not focused on your current behavior and decision-making.”
In practical terms, a shopper who buys a table will likely see more tables, even after completing the purchase. At the same time, countless listings remain buried because they never gain enough momentum to appear in algorithmic rankings. Buyers experience repetition. Sellers struggle for visibility.
The “AI Platform for Real-Time Discovery”
Albatross describes its system as an “AI platform for real-time discovery.” Instead of focusing only on historical data, the company uses sequential AI techniques.
Kevin Kahn explains the concept clearly:
“In language, words carry meaning through their order, but in discovery, events carry meaning through their sequence. We are making sense out of users’ behaviours in real time.”
Rather than relying on large language models commonly associated with generative AI, the platform interprets the order and context of user actions. Each click, scroll, and interaction reshapes the recommendations instantly.
As a result, recommendations update continuously, more diverse products appear in front of users, and the system adjusts instantly as shopper intent changes.
Kahn, who previously led machine learning and recommendation platforms at Amazon Music, emphasizes that the model centers the experience around the user’s immediate signals rather than outdated assumptions.
Investment, Expansion, and Recognition
Founded in 2024, Albatross has secured a total of $16 million in funding, with more than $12 million raised toward the end of last year alone. The company’s backers include MMC Ventures, Redalpine, and Daphni, reflecting strong investor confidence in its approach to AI-driven product discovery.
The company also received a Tech for Retail Innovation Award last year, highlighting growing industry interest in improving product discovery systems.
Meanwhile, Wallapop was recently acquired by Naver, a South Korean search engine business. This acquisition may open pathways for international scaling of the Albatross technology.
Rising Frustration Across E-Commerce
Albatross enters the market at a time when dissatisfaction with search and discovery tools is increasing.
One survey found that nearly 20% of e-commerce websites failed to deliver results for complex search queries. That gap leaves shoppers without answers and pushes them toward competing platforms.
Consumer sentiment reflects similar concerns. Research published last year reported that 80% of shoppers were unhappy with product search and recommendation results on retail and marketplace platforms.
At the same time, research from McKinsey & Company estimates that generative AI could contribute up to $275 billion in operating profits to the fashion and luxury sectors over the next three to five years. While many companies explore generative AI, Albatross focuses instead on behavior-driven sequencing models.
The Future of Product Discovery
Product search now plays a direct role in how consumers evaluate options and make purchase decisions. When recommendations adjust instantly to user behavior, engagement improves. When results repeat or miss intent, frustration follows.
Albatross builds its platform around real-time behavioral sequencing rather than static historical data. Early results from its partnership with Wallapop show higher engagement, more interactions, and stronger purchase intent.
As e-commerce expands and 74% of Spaniards plan to combine online and in-store shopping in 2024, expectations for seamless discovery continue to rise. Marketplaces that respond to live user signals and surface a broader range of products will be better positioned to retain both buyers and sellers. Sequence-based AI signals a clear shift in how digital commerce platforms approach product discovery.
