Things I have built.
Things I am building.

Finance recruiting nearly broke me. Not because I wasn't capable - but because the system isn't built for people like me. No alumni network from a target school. No older brothers who interned at Goldman. No prep course that anyone in my circle had taken. I had to figure it out at 11 PM, watching YouTube videos and decoding Reddit threads the night before first-round interviews.

That is not how this should work.

The problem: The information asymmetry in finance recruiting is staggering. Kids at target schools with prep resources, alumni mentors, and institutional knowledge walk into recruiting season with a fundamentally different toolkit than first-generation or international students. The result is that finance careers disproportionately go to candidates who were already well-resourced - which is not meritocracy. It's a network effect.

The solution: Wallstreet.ai is the platform I wish had existed. It uses AI to help students prep for finance interviews - behavioral and technical - simulate real interview scenarios, and get specific feedback on their responses. It optimizes resumes for specific firms and roles, because the way you describe an experience matters enormously in finance: the language, the metrics, the framing. And it keeps users informed on deal flow, market trends, and company profiles so they can walk into any conversation with genuine context, not memorized talking points.

The vision: Democratize access to finance careers - especially for LATAM and first-generation candidates. Talent is evenly distributed. Opportunity is not. AI can serve as a personalized coach for anyone with an internet connection. There's no reason prep quality should correlate with zip code or alumni connections.

The platform is early-stage. But the problem is real, the demand is there, and every time I talk to another finance student who navigated recruiting the way I did - alone, underresourced, figuring it out at midnight - I feel the urgency sharpen.

Most fitness apps track your workouts. AthletIQ runs them. The insight behind the product is simple: generic training doesn't work for athletes or serious gym users who want to actually perform, not just move. Every body is different. Every week is different. Your app should know that.

The problem: The market is flooded with apps that log your reps and give you a cookie-cutter program. None of them adapt. None of them think. You end up with too much data and no clear signal on what to actually do today. Especially if you play a sport, the disconnect between your training app and your game day performance is almost comical.

The solution: AthletIQ gives you a daily training plan that adjusts in real time based on your recovery, your performance trends, and your goals. A readiness score tells you whether to push hard, go moderate, or recover. An AI coach you can actually talk to answers questions like "what should I train today?" and adjusts the plan instantly. Sport-specific modes for rugby, soccer, gym, and general fitness mean the programming actually reflects how your body is being used.

The build: Started in January 2026. The dashboard shows your active sport mode, today's focus session, AI coach insights, and key performance stats. Strength trends, vertical jump, sprint times: everything in one place, with context. Target launch mid-July 2026.

Value proposition: "AthletIQ doesn't track your fitness. It runs it."

My summer at Avolta - the world's largest travel retail company - was my first real taste of global business operations at scale. Assigned to the cosmetic category management team for Latin America, I spent the summer inside the data: perfume sales across airport locations in Panama, Ecuador, Colombia, and Brazil.

The task: figure out which SKUs were underperforming and why. Then make recommendations on product selection and margin optimization. Simple enough on paper. In practice, it means understanding the nuances of a duty-free shopper in Panama City versus one in Bogotá - different purchasing habits, different price sensitivities, different brand affinities - and building an argument from the data upward.

I learned that luxury retail is not just about beautiful packaging and aspirational marketing. It's a data game. Which brands drive margin? Which locations over-index on certain price points? Where is conversion actually happening? I built pricing analyses and helped structure brand negotiation frameworks - the kind of analysis that feeds directly into decisions about which luxury brands get shelf space and at what terms.

Avolta taught me what it looks like to operate at scale: multiple countries, multiple currencies, multiple stakeholders, all at once. It also taught me that good category management is equal parts art and science - you need to know your customer, your competitor, and your data simultaneously, and move between all three without losing the strategic thread.

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One Padel Indoor Club gave me something you cannot get in a classroom: the productive chaos of an early-stage business. I joined before the doors opened - helping with business planning, marketing strategy, and the operational groundwork of building a brand from scratch in a city that was just beginning to discover padel.

The reality of a startup: there is no playbook. You tracked financial transactions one day and coordinated a community tournament the next. You helped build the social media presence, managed vendor relationships, and then stayed late to figure out why the booking system wasn't working. The diversity of the work wasn't a distraction - it was the education.

Working without structure forced a kind of resourcefulness that structured environments rarely produce. When there's no precedent for a decision, you have to trust your judgment, move quickly, and learn from what doesn't land. That instinct - operating comfortably in ambiguity, executing before all the information is in - is something I now carry into every professional context I enter.

Padel was growing fast in Miami, and being inside a business trying to capture that wave early taught me a lot about timing, brand positioning, and what it takes to build community around a product. You can have the best facility in the city. If people don't feel ownership over the experience, they won't come back. We worked hard to make sure they did.

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