About Zevi
Hi, I’m Zevi Sternlicht.
Before I started building LEGO portfolios, I built a platform called Reeclaim. It helped commuters across the UK get compensation for delayed journeys automatically. Users didn’t need to lift a finger. We connected to their travel accounts, monitored journeys, and submitted claims whenever they were due refunds.
Reeclaim was picked up by Forbes, but also featured in the Evening Standard, MoneySavingExpert, and other outlets. Martin Lewis even spoke about it on national TV. Behind the scenes, Reeclaim was analysing hundreds of millions of journeys and using that data to determine, often to the minute, who was owed what. That’s how we delivered refunds most people didn’t even realise they were entitled to.
I’m now applying those same skills to something else that’s quietly been growing in value over the past two decades. I’m talking of course about LEGO.
Why LEGO
I started tracking LEGO sets about five years ago. At first, it was a curiosity. I noticed that some boxes were appreciating fast after retirement. Not just by a few pounds, but rather by 100%, 200%, sometimes more. What started as a side project turned into an obsession. I built models, collected data, started making predictions, and tested them with small real-money portfolios. Over time, patterns became clearer, and the results got stronger.
I’ve helped friends and family see returns of over 300% on some of these investments. Until now, I kept this to myself.
What I Do Now
I’m now offering this as a service to investors who wish to diversify their portfolio by owning alternative investments. Whether you’re investing £5,000, £50,000 or even £500,000, I’ll build you a portfolio using the same tools and logic I’ve been refining for years.
The LEGO data I’ve collected spans thousands of sets, millions of transactions, and a huge amount of pricing and performance data across different platforms. I look at what themes tend to perform, how long sets stay in production, whether they were heavily discounted, and which ones have rare components. There is no knowing with certainty exactly which models will rise to staggering heights, but using past data patterns, we can predict which sets are likely to rise.