Raw pace and power data overwhelmed users. AI now turns millions of daily uploads into instant, encouraging performance insights.
A global fitness platform serving over 150 million users across 185 countries, processing tens of millions of daily activity uploads covering more than 50 sport types.
Users felt overwhelmed by raw data points like pace and power, struggling to understand how specific efforts compared to their historical...
“The spirit of making data more accessible and meaningful for our users is at the core of our new feature. Our goal is to improve the athlete experience by increasing data accessibility and evolving these capabilities over time.”
Fitness tracking and social networking platform for athletes and outdoor activities.
Cloud computing platform and on-demand infrastructure services.
Strava's Workout performance analysis is part of this use case:
Related implementations across industries and use cases
Player data scattered across systems; every insight required manual work. Now coaches query 1,000+ data points per match in plain language.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Generic energy tips ignored smart meter data. Now, AI analyzes 48 daily readings per customer to write personalized savings advice.
Classrooms with 1:30 ratios left students behind. Now, 350M monthly queries route to specialized models for personalized tutoring.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Raw pace and power data overwhelmed users. AI now turns millions of daily uploads into instant, encouraging performance insights.
A global fitness platform serving over 150 million users across 185 countries, processing tens of millions of daily activity uploads covering more than 50 sport types.
Users felt overwhelmed by raw data points like pace and power, struggling to understand how specific efforts compared to their historical...
“The spirit of making data more accessible and meaningful for our users is at the core of our new feature. Our goal is to improve the athlete experience by increasing data accessibility and evolving these capabilities over time.”
Fitness tracking and social networking platform for athletes and outdoor activities.
Cloud computing platform and on-demand infrastructure services.
Strava's Workout performance analysis is part of this use case:
Related implementations across industries and use cases
Player data scattered across systems; every insight required manual work. Now coaches query 1,000+ data points per match in plain language.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Generic energy tips ignored smart meter data. Now, AI analyzes 48 daily readings per customer to write personalized savings advice.
Classrooms with 1:30 ratios left students behind. Now, 350M monthly queries route to specialized models for personalized tutoring.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.