For years, we would finish a squash match and ask the same questions.
Why did we lose that game?
Why did the same error keep happening?
Was the opponent actually better, or were we just playing the wrong way?
The answers usually came from memory, a coach's observations, or hours of manually rewatching video.
But the entire match was already on camera.
Every rally. Every movement. Every decision. Every mistake.
So we kept thinking:
Why can't software actually understand it?
That question became Core Sports AI.
We don't need more stats
Sports technology has gotten very good at collecting data.
But collecting data and understanding performance are two different things.
A player doesn't really care that they hit 14 drops or spent 42% of a rally in one part of the court.
They care about what that means.
Why did I keep losing rallies from that position?
Why am I making the same mistake under pressure?
What has actually improved over the last three months?
What should I work on tomorrow?
And if I'm playing someone next weekend:
How do I beat them?
That is the problem we are trying to solve.
Core turns match video into performance intelligence.
We track what happens in the match, but the goal is not to stop at a report.
The goal is to understand the athlete.
What if your AI actually knew your game?
Most analysis starts over every time you upload a new match.
We think that is backwards.
Your performance system should remember.
It should know what you struggled with five matches ago.
It should recognize that a weakness is starting to disappear.
It should know which patterns keep showing up when you are under pressure.
It should understand how your movement, shot selection, positioning, and decision-making are changing over time.
That is why we are building the Core Digital Twin.
Every match gives Core another piece of the athlete.
One match can tell us what happened that day.
Twenty matches can start telling us who the player is.
The more Core sees, the more complete that picture becomes.
Our vision is for every athlete to eventually have an AI model of their game that grows with them throughout their career.
Scouting should be personal
We think opponent preparation has the same problem.
Today, scouting often means finding footage, watching it manually, taking notes, and trying to figure out which tendencies matter.
But knowing an opponent's weaknesses is only half the answer.
A weakness is useful only if you know how to exploit it with your own game.
That is why Core Scouting is connected to the player.
You can upload a match of someone you have never played before.
Core analyzes how that opponent plays, then combines that with what it already knows about you.
Instead of only saying:
"Here is what this opponent does."
We want Core to say:
"Here is how you should play them."
That is a very different kind of sports analytics.
Why start with squash?
Because we know the sport deeply.
Squash is fast, tactical, physical, and incredibly difficult to analyze.
Players cross constantly. The ball moves quickly. Rallies change direction in seconds. Small decisions in positioning or shot selection can completely change the point.
It is a difficult computer vision problem.
It is also a sport where we have personally experienced the problem we are solving.
We know what it feels like to finish a match and wonder what actually happened.
We know what it feels like to prepare for an opponent by watching hours of footage.
We know how valuable a great coach can be, and we also know that no coach can remember every rally an athlete has ever played.
We are not trying to replace coaching.
We are trying to give coaches and athletes a level of memory and understanding that was never possible before.
Squash is the starting point
We are starting with squash because it is where we can build the deepest product and learn the fastest.
But we believe the idea is much bigger.
Imagine an athlete starting at 12 years old with an AI that grows alongside them.
It watches their matches.
It remembers their tournaments.
It knows what they struggled with last season.
It knows what changed.
It knows what kind of opponents create problems for them.
It knows whether the thing they have been working on in training is actually showing up in competition.
And before their next match, it already knows what they should focus on.
That is what we mean by AI performance intelligence.
Not another dashboard.
Not another collection of statistics.
An intelligence layer that actually knows the athlete.
What we are building toward
We want Core to become the place an athlete goes after every match and before the next one.
A system that can answer:
What happened?
Why did it happen?
What is changing?
What should I work on?
How should I play my next opponent?
And over time, those answers should become more personal because Core has seen the journey that came before them.
We want every athlete to have an AI that knows their game as well as they do — and eventually, better.
We're starting with squash.
Welcome to Core.
Core turns a squash match into a model of how you play.
See what Core gives you