Behind the Scenes: How We Built and Scaled Our AI-Powered Recommendation Engine

Published 2024-09-10 · Updated 2026-05-23 · 6 min read · Leadership in AI Era · By Sahin Boydas

People always ask me how we built our recommendation engine. This is the real, behind-the-scenes story of the technical challenges, the team dynamics, and the leadership decisions that made it all possible.

Most of what you've read about behind the scenes: how we built and scaled is wrong. I know because I believed it too, and it cost me.

People always ask me how we built our recommendation engine. This is the real, behind-the-scenes story of the technical challenges, the team dynamics, and the leadership decisions that made it all possible.

The Reality Nobody Talks About

Most people approach behind the scenes: how we built and scaled with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.

The first thing to understand is that most founders overthink this and underspend on execution. I've seen this play out across dozens of companies. The pattern is unmistakable.

At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that you need to move fast and break things. Once we made the switch, everything changed.

Why Most Approaches Fail

Let me be direct: about 70% of the approaches I see to behind the scenes: how we built and scaled are fundamentally flawed. Not slightly off. Fundamentally flawed.

The root cause is usually one of three things:

  • Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
  • Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
  • Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.

Lessons From the Trenches

I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.

Lesson 1: The best time to start thinking about behind the scenes: how we built and scaled was yesterday. The second best time is now. Don't wait until you have the perfect plan.

Lesson 2: Hire for attitude, train for skill. The best behind the scenes: how we built and scaled practitioners I've met weren't the most technically gifted. They were the most curious and persistent.

Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.

This connects to broader themes around AI transformation leadership, chief AI officer, AI leadership skills that I've been thinking about a lot lately.

What's Next

The world of behind the scenes: how we built and scaled is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.

My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.

And if you're a founder building in this space, remember that the best time to get behind the scenes: how we built and scaled right is before you need to. Don't wait for a crisis to force your hand.

I'll keep sharing what I learn. This stuff matters too much to keep to myself.

Frequently Asked Questions

How can I apply this thinking to my own situation?

Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.

Do all experts agree with this view?

No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.

What experience informs this perspective?

This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.

What's the most common pushback you get on this?

People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.

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