She fixes businesses. Using AI. Without the hype.
Sofia spent years building AI systems for some of the biggest companies in the world. It was interesting work. But it was also far away from the actual problem.
She wanted to work with real people. Founders who were stressed. Teams who were drowning. Businesses where fixing something actually made a difference to someone's Monday morning.
She also just wanted to build something of her own.
And she kept noticing the same thing — the businesses that needed help the most had nobody to call. The big firms only wanted big clients. The cheap agencies made big promises and disappeared. Nobody was doing it simply, honestly, and properly.
So she started Krib to do exactly that.
Sofia is the kind of person who walks into a business and immediately starts noticing what's broken. Not in a critical way — she just can't help it. She's wired to see how things work and how they could work better.
She reads a lot. Not just about AI and technology — about business, people, and how things are built. She's always trying to understand things more deeply.
She cares about doing things properly. Not perfectly — but properly. There's a difference. Proper means it actually works. Proper means the person on the other end of the work is better off because of it.
She hates jargon. She thinks if you can't explain something simply, you probably don't understand it well enough yet. That's why she built Krib the way she did — no buzzwords, no hype, no unnecessary complexity.
And she genuinely cares about the people she works with. Not just the work. The actual humans running the businesses, dealing with the chaos, trying to build something good.
She works from wherever she happens to be. Not because it sounds cool — just because she doesn't see the point of being tied to one desk in one city when the work can be done from anywhere.
She spent years working on AI and data projects for some of the largest and most complex organisations in the world. She's seen what good systems look like. She's seen what bad ones look like. And she's learned how to tell the difference — and explain it in plain language.