About Us
We started Coreio because watching brilliant machine learning projects die in production broke our hearts. You'd pour months into training a killer model, then... nothing. It'd sit there, degrading, ignored, forgotten. That's the gap we fill. We're obsessed with the messy middle part — the stuff between building something cool and actually running it reliably. Our 23 engineers and data scientists live and breathe MLOps because we genuinely believe that's where the magic happens. Not in the algorithms. In keeping them alive, thriving, and constantly improving.
Over time, we’ve built a reputation for reliability, quality, and clear communication. We treat each project as a partnership and aim for outcomes that last.
Our Approach
Our approach combines proven methods with modern tools. We start with discovery, align on goals, plan clearly, and execute with quality checks at every step.
Why Choose Us
Experienced Team
Our professionals have decades of combined experience. We've seen it all and know how to handle any challenge.
Quality Guaranteed
We stand behind our work with comprehensive guarantees. If you're not satisfied, we'll make it right.
Fair Pricing
Transparent, competitive pricing with no hidden fees. You'll always know exactly what you're paying for.
Proven Results
Thousands of satisfied customers and a track record of successful projects speak for themselves.
Our Story
Coreio started in 2019 when three of us were managing ML systems at a fintech startup. We'd built something clever, but keeping it running was absolute chaos. Model drift? Check. Data pipeline failures? Daily. Impossible to debug. We kept thinking: there's got to be a better way. So we quit and built it. Our first real customer signed on in March 2020 (worst possible timing, obviously), but they stuck with us. Then another. Then another. Today we're working with 47 organizations across Europe, from scrappy startups to enterprises that definitely should've built this themselves.
Company founded with a team of 3 passionate professionals
Expanded services and reached 500+ satisfied customers
Opened new headquarters and doubled our team size
Celebrated serving over 2,000 clients with 98% satisfaction rate
Meet Our Team
The people behind Coreio
We’re a team that values ownership, clarity, and growth. We celebrate wins, learn fast, and keep client outcomes at the center.
Elena Popescu
Co-founder & Chief Technical Officer
Built ML systems for three startups before getting fed up with chaos. She's obsessed with monitoring and has strong opinions about data pipelines. Speaks five languages, none of them SQL.
Mihai Gheorghiu
Co-founder & Operations Lead
Former infrastructure engineer who realized most MLOps problems aren't technical—they're organizational. Believes in documentation. Actually maintains a personal wiki. Drinks coffee constantly.
Andrei Vasile
Senior ML Engineer
Spent eight years at a major search company optimizing recommendation systems. Brings battle-tested thinking to every problem. Mentors younger engineers relentlessly. Hikes obsessively on weekends.
Our Mission
Help teams deploy and maintain machine learning systems that actually work. We provide the infrastructure, monitoring, and automation so you can focus on innovation instead of firefighting. Because your models deserve better than spreadsheets and crossed fingers.
Our Vision
A world where deploying ML feels as natural as deploying code. Where every organization — whether they've got 5 data scientists or 50 — can build, monitor, and iterate on models without drowning in complexity. That's what we're chasing.
Our Core Values
The principles that guide everything we do
Brutal Honesty
We tell clients what we actually think. Sometimes that means saying 'you don't need MLOps yet' or 'this approach won't work for your scale.' Trust matters more than a quick deal.
Obsess Over the Details
ML ops is 90% unglamorous grunt work. We love it. Monitoring latency at the 99.9th percentile. Tracking data quality across 14 sources. That's where real value lives.
Solve Real Problems
We don't ship features because they look good in a demo. Every tool we build solves something that actually keeps customers up at night.
Keep Learning
ML moves fast. So do we. We dedicate 20% of our time to staying sharp — reading papers, experimenting, arguing about new techniques. Stagnation kills innovation.