Most SaaS products aren't technological marvels.

The real moat isn't in the code

It's in solving problems well in ways that are hard to replicate.

Don't confuse vibe coding with actual engineering.

In this episode, we sit down with Rami, co-founder of Querio, for an in-depth exploration of AI-native data analytics, the future of business intelligence, and the surprising journey from Amazon warehouse optimisation to building a revolutionary data platform. Discover how his unconventional path from winning a US green card lottery to saving Amazon $55 million annually shaped his contrarian views on AI development and why he believes most companies are fundamentally broken when it comes to data access.

🎯 On the Docket:

00:07:20 - From Lebanon to Texas: The green card lottery story that changed everything

00:11:01 - Community college to Amazon: Building resilience through adversity

00:19:20 - Inside Amazon's warehouse evolution: Optimising 2000 Kiva robots

00:24:00 - The $55 million algorithm: How first-principles thinking drives innovation

00:41:00 - Why "vibe coding" won't replace engineers (and why that's good)

00:46:28 - Querio AI-native approach: Beyond chatbots to functional data products

00:49:00 - Demystifying large language models: Why they're simpler than you think

01:05:11 - The art of balancing AI automation with manual controls

⚡ Key Insights:

Why most SaaS products aren't technological marvels (and what that means for AI)

The critical difference between AI-native products and AI-bolted-onto-existing-products

How Query solved the "5-week data team ticket" problem plaguing the enterprise

Why understanding LLMs as "next word predictors" makes them less magical and more useful

The psychology behind effective product design (the Dallas airport baggage claim lesson)

How authentic relationship building creates compound opportunities over time

Why technical founders need to balance curiosity with execution focus

🔗 Where to find Rami

LinkedIn

Querio