The Ultimate List of 340+ Real-World AI Systems 🧠⚙️

How the world’s most important companies actually use AI - in products, payments, risk, healthcare, and everyday decisions 🤖📊

👋 Hey, Linas here! Welcome to another special issue of my daily newsletter. Each day, I focus on 3 stories that are making a difference in the financial technology space. Coupled with things worth watching & the most important money movements, it’s the only newsletter you need for all things when Finance meets Tech. If you’re reading this for the first time, it’s a brilliant opportunity to join a community of 360k+ FinTech leaders:

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Most conversations about AI today are still about what’s possible.

This is about what’s already running - at scale, under pressure, with real consequences.

I’ve curated 340+ real-world AI systems deployed in production, the kind that survive latency budgets, regulatory scrutiny, edge cases, and tens of millions of real users.

Not demos.

Not research toys.

Not “AI-powered” slides.

These are the systems built by:

• Tech giants like Google, Apple, and Amazon - optimizing search, recommendations, language, vision, and on-device intelligence.

• FinTech heavyweights such as Stripe, Adyen, Brex, Monzo, and PayPal - deploying ML for fraud detection, risk scoring, authorization, compliance, and revenue protection.

• LatAm champions like Nubank and Mercado Libre that are using AI at a massive scale for credit, personalization, logistics, and financial inclusion.

Across tech, finance, healthcare, marketplaces, SaaS, and consumer apps, each case study goes deep on a single production system:

• The exact user problem it solves

• The ML approach (NLP, computer vision, search & ranking, recommender systems, fraud, forecasting, and more)

• The model design & evaluation metrics that actually mattered

• The deployment architecture that made it reliable in the real world

We’re not talking theory here. It’s how AI behaves once it’s accountable to uptime, revenue, and risk.

Every entry is sourced from first-hand engineering blogs, papers, or internal write-ups, meaning you’re learning directly from teams who shipped, broke things, fixed them, and scaled anyway.

If you’re a founder, operator, product leader, or engineer, this is basically the ultimate map of how modern AI is actually built.

Let’s dive in and examine the systems that are already shaping how the AI-first economy actually operates.