AI development for sports products
AI is useful in sports where it removes work people already do by hand: writing the recap, tagging the film, building the schedule, answering the same question about the stats for the tenth time.
We build those features into new and existing sports products. The focus is on a specific task with a measurable result, grounded in your own data, with a person in the loop where accuracy matters.
Trusted By
What We Build
Automated recaps and insights
Game summaries, player notes, and reports generated from box scores, play-by-play, and tracking data.
Computer vision for video
Automatic tagging, clipping, and highlight detection so film is searchable without hours of manual work.
Smart scheduling
Schedules that respect venues, travel, rest, and officials, and can be rebuilt quickly when something changes.
Chat on your own data
Assistants that answer questions from your stats, rulebook, or content library, with sources shown.
Content and tagging automation
Summaries, metadata, and image selection for editorial teams publishing at game speed.
Who It's For
- Sports platforms adding AI to an existing product
- Media and content teams automating routine coverage
- Leagues and clubs with video archives that nobody can search
- Founders building an AI-first sports product
How We Work
Scope
A working session to pin down the user, the one job the product has to do, and what can wait. You leave with a written scope and a build plan.
Build
Senior engineers ship in short cycles, so you review working software as it takes shape instead of status decks.
Launch
App store submission, hosting, monitoring, and a launch-day checklist, so the first real users have a clean experience.
Grow
Stay on with the same team for the next release, or hand the product off to your own engineers with documented code.
Questions We Get
Where does AI actually help in a sports product?
Where there is repetitive work on structured data or video: recaps from box scores, tagging film, drafting schedules, answering stat questions. It helps least where the input data is thin or the output cannot be checked.
How do you keep AI output accurate?
By grounding it in your data rather than the model's memory, testing against real games before launch, showing sources, and keeping editorial or coaching review in the workflow where a mistake would matter.
Can you add AI features to a product we already have?
Yes. Most of this work is integration: connecting models to the data and workflows your product already has, then shipping it as a feature your users can rely on.
Related Services
Data & Analytics
Performance dashboards, tracking platforms, and analytics products that turn raw sports data into decisions.
MVP Development
Go from idea to a clickable, deploy-ready MVP in weeks. We build the first version that gets you in front of users and investors.
Fan Engagement
Community, content, and gameday products that keep fans coming back — from leaderboards to live experiences.
Have a project in mind?
Book a 30-minute call and we will map out how to ship it.