Player data scattered across systems; every insight required manual work. Now coaches query 1,000+ data points per match in plain language.
National governing body for one of rugby's most dominant programs, with national squads winning 76-82% of their matches and more than 1,000 performance data points collected per player per game, competing in an elite environment where even marginal gains determine outcomes.
Performance data was spread across fragmented systems with no centralized view of player histories, and workflows for surfacing insights were largely...
“We needed a data environment that increased our nimbleness and gave insights that could support decision-making throughout the performance week.”
Governing body for rugby union in New Zealand.
Cloud computing platform and on-demand infrastructure services.
New Zealand Rugby's Player performance analysis is part of this use case:
Related implementations across industries and use cases
Offshore conditions blocked live data. Now, wearables stream telemetry to AI, giving commentators instant insights in seconds.
Rugby match data existed but never reached fans live. AI now turns it into 65+ on-screen insights per game, built and approved by analysts.
Fans tracked picks on static lists. Now, a GenAI assistant answers questions with real-time stats, driving 1M+ engagements.
Analyzing new segments required manually tagging 50K reviews. Now, AI automatically translates and categorizes multilingual feedback.
Coaches had 30 seconds between points, but insights took hours. Now, AI analyzes live telemetry to answer tactical questions courtside.
Translating updates one language at a time bottlenecked distribution. Now, AI drafts multilingual commentary in minutes for editor review.
Stats were trapped in dropdown menus. Now, AI unifies live data to answer complex questions instantly—no searching required.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Player data scattered across systems; every insight required manual work. Now coaches query 1,000+ data points per match in plain language.
National governing body for one of rugby's most dominant programs, with national squads winning 76-82% of their matches and more than 1,000 performance data points collected per player per game, competing in an elite environment where even marginal gains determine outcomes.
Performance data was spread across fragmented systems with no centralized view of player histories, and workflows for surfacing insights were largely...
“We needed a data environment that increased our nimbleness and gave insights that could support decision-making throughout the performance week.”
Governing body for rugby union in New Zealand.
Cloud computing platform and on-demand infrastructure services.
New Zealand Rugby's Player performance analysis is part of this use case:
Related implementations across industries and use cases
Offshore conditions blocked live data. Now, wearables stream telemetry to AI, giving commentators instant insights in seconds.
Rugby match data existed but never reached fans live. AI now turns it into 65+ on-screen insights per game, built and approved by analysts.
Fans tracked picks on static lists. Now, a GenAI assistant answers questions with real-time stats, driving 1M+ engagements.
Analyzing new segments required manually tagging 50K reviews. Now, AI automatically translates and categorizes multilingual feedback.
Coaches had 30 seconds between points, but insights took hours. Now, AI analyzes live telemetry to answer tactical questions courtside.
Translating updates one language at a time bottlenecked distribution. Now, AI drafts multilingual commentary in minutes for editor review.
Stats were trapped in dropdown menus. Now, AI unifies live data to answer complex questions instantly—no searching required.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.