Start with real runs
A recent Garmin or Stryd activity carries more context than a generic quiz or a few observed steps.
Founder of Run-It · Belgium
Brecht Colemont
I’m a 27-year-old engineer from Belgium, a marathon and ultramarathon runner, and the founder of Run-It. I built it because buying running shoes still felt like guesswork, even after years of running and access to more data than ever.
Profile reviewed September 2, 2026.
Why I built Run-It
My running improved. Choosing shoes did not.
When I started running, the goal was simply to become consistent. That grew into multiple marathons and ultramarathons. My training became more structured, my watch collected more data, and I learned far more about how I moved.
Yet buying running shoes stayed difficult. A store analysis often reduced the decision to pronation or a short treadmill snapshot. That could add context, but it never answered the full question for me: which shoe makes sense for the way I run across real training?
Running-shoe reviews could be detailed and honest, yet they still described the reviewer’s experience. A shoe that felt brilliant to one runner could feel completely different to another. I wanted a starting point built from the runner’s own data, not another universal verdict.
Run-It grew from that frustration: even today, there is no reliable way to know exactly which running shoe will work for you before you buy it.
Engineer, runner, founder
The experience behind the product
My engineering background shapes how I work: define the problem, test assumptions, document the method, and make the limitations visible. Four years at EY gave me the confidence to build in a structured way. After developing Run-It alongside my job for roughly a year and a half, I left to work on it full-time.
View my public work history →Engineer
I bring an engineering and product-development perspective to a problem that is often communicated through broad labels.
Distance runner
Multiple marathons and ultramarathons gave me years of real training, shoe rotations, long-run fatigue, and expensive trial and error.
Full-time Run-It founder
I build the product and its running-data methodology, and I write or review the guides that explain how those decisions are made.
Methodology
How I approach a running-shoe recommendation
The goal is not to turn one metric into a verdict. It is to combine useful signals, preserve context, and tell the runner exactly where the recommendation ends.
Use composite signals
Run-It uses multiple running-dynamics inputs and LPI rather than treating pronation as the decision rule.
Keep sizing traceable
Fit guidance refers to the relevant official brand sizing chart instead of inventing a universal conversion.
State the limits
A good outcome is a satisfied runner: a shoe that feels comfortable across real runs. That aligns with the footwear-research idea of a comfort filter and preferred movement path, rather than matching someone to a pronation category or prescribed gait pattern. Read the comfort analysis.
Outcome data
A better shoe choice is a probability problem
You only learn whether a running shoe works for you after a real run, and ideally after several. Run-It uses your running data, the model’s characteristics, and the relevant official sizing chart to give you the strongest data-backed starting point for both shoe choice and fit.
Test the method
Positive outcomes are the benchmark
We want to measure whether Run-It recommendations lead to more positive shoe ratings than other ways of buying shoes. That is the standard: test the recommendation against real runner outcomes, learn from those results, and continuously improve it to give every runner a higher chance of finding a shoe that works for them.
