Data Graphs connects the data, content, audience, rights, and commercial relationships across your sports ecosystem, giving teams, leagues, federations, and associations shared context for AI, agents, and the people running the business.
Sports organizations already run on specialist systems for competition data, editorial, audience analytics, marketing, advertising, sponsorship, rights, ticketing and commerce.
The difficult questions are rarely contained in one of them.
Data Graphs connects the relationships and business rules across those systems into a shared context layer that people, applications, and AI agents can use.
“What content can we offer this sponsor today?”
Answering that question can require context from across the business:
Which player, match, and sporting moment is relevant?
Which content is connected to that moment?
Which content does this audience actually engage with?
Can this content be used here, on this channel, today?
What does the partnership allow?
What can we actually offer?
Is a competitor already attached?
Access gets AI to the data. Context lets AI reason across it.
Give copilots and AI agents a connected, governed model of your sports world, including the relationships and rules that sit between systems. Data Graphs is MCP-native, giving AI agents governed access to the same shared business context through standard MCP services.
The result is AI that can move from a question to a grounded answer across multiple business domains.
Connect editorial intent with recorded audience behavior, reporting periods, channels, and sporting context.
Understand which content different audience segments actually engage with and connect that behavior back to the players, teams, matches and moments behind it.
Connect sponsorship, advertising inventory, audience performance and the sporting context behind the opportunity.
A commercial question can start with an over-performing campaign and move through audience, content, player, available inventory and rights before an offer is made.
Bring territory, channel, permitted use, validity windows and commercial conflicts into the answer before content is recommended.
AI can identify what qualifies, what gets ruled out and which rule caused the decision.
The team behind Data Graphs helped connect athletes, countries, events and editorial content for BBC Sport and the London 2012 Olympics at major-event scale.
Our team also contributed to the development of the IPTC Sport Schema, the open industry standard for modeling sports data across team and individual competitions.
That work established a principle that still shapes Data Graphs today: model the relationships across a sports domain and make that context usable wherever it is needed.
Today, the same foundation gives AI something it rarely has on its own: an understanding of how the sports business fits together.
For the World Cup, Data Graphs created a connected sports model using live sports data and the IPTC Sport Schema, then extended that context across editorial, audience analytics, marketing, advertising, sales, sponsorship and rights.
That means a question can begin with a sporting moment and continue into the business context around it, or start with an audience or commercial requirement and work back to the sport.
During the Round of 16, which player profiles showed the biggest difference in engagement between US die-hards and Mexico followers? Show the engagement evidence and connect each result to the relevant player, team, and match.
Our quarter-final campaign over-performed. Show me available inventory for the same audience, using content about players that audience engaged with, and only include video we can use on US social.
Which Mexico videos can we offer a sponsor for its US social activation today, and why are the other clips unavailable?
These questions depend on relationships and business rules that sit across multiple systems. Data Graphs makes that connected context available to AI and agents.
Connect fan, content, venue, ticketing, sponsorship and commercial context around the team and the moments that matter.
Connect competition, team, player, content, rights, audience and commercial context across the league ecosystem.
Create shared context across disciplines, competitions, athletes, member organizations, archives, rights and partners while supporting different markets, teams and applications.
Data Graphs gives teams across the organization a shared understanding of the relationships between sport, content, audiences, rights and commercial activity.
Bring together the context behind a player, moment, audience, piece of content, partner or opportunity, even when the underlying data lives across different systems.
Ask questions that cross editorial, analytics, marketing, rights, sponsorship, advertising and sales without manually piecing the answer together.
Understand what content is relevant, who it matters to, where it can be used, what a partnership allows, and which opportunities are actually available.
Give AI access to the relationships, rules, and permissions behind the business so it can reason from trusted context and support governed action. MCP-native access makes that context available to agents across tools and workflows.
Data Graphs works across your existing technology stack, creating a shared context layer without forcing teams into another operational system.
For technical teams: Knowledge graphs · Hybrid GraphRAG · MCP · GQL / OpenCypher · APIs
Start with the systems and data already in place. Data Graphs connects the relationships and rules between them so people, applications, and AI agents can work from the same business context.
Data Graphs. Context wins.