The Nielsen-owned content intelligence business is building a modular AI stack designed to make sports discovery, media operations, and content decision-making more accurate, faster, and easier to deploy at scale.

Gracenote is expanding its AI product roadmap with a dedicated Sports MCP Server, an entertainment-focused small language model, and task-oriented agents aimed at media companies, streaming platforms, and sports-focused viewer experiences.

The new roadmap is built around the company’s existing Video Model Context Protocol Server, which connects customers’ preferred AI models to Gracenote’s source-verified entertainment metadata. The planned components can be used independently or combined, giving media organizations a way to build AI workflows without developing every application from scratch.

The strategy addresses a central issue in media AI: general-purpose models can produce fluent answers, but they are not always reliable enough for content discovery, rights management, catalog operations, and live sports information.

Gracenote said its June 2026 research evaluated an LLM’s output across 2,600 popular film and television titles in 13 countries. For 506 titles—nearly one in five—the model fabricated every metadata attribute examined.

That accuracy challenge is particularly relevant in sports, where schedules, scores, standings, statistics, availability, and where-to-watch information change constantly.

The planned Sports MCP Server will extend Gracenote’s existing video-data infrastructure with information on live, upcoming, and recent sports events. The company says the server will support schedules, scores, standings, statistics, and viewing availability, creating a structured foundation for conversational search, personalized recommendations, and fan questions about teams and athletes.

The goal is not simply to place a chatbot on top of sports data. It is to give AI systems access to verified, continuously updated information and purpose-built tools that can operate across a media company’s existing workflows.

Gracenote already maintains source-verified metadata covering more than 55 million titles, along with continually updated viewing-availability data. Its entertainment knowledge graph maps relationships among programs, cast, and crew, while persistent content IDs connect records across sources and services.

The company is also planning an entertainment-focused small language model, or SLM, for high-volume tasks where low latency and predictable costs matter. Potential applications include evaluating acquisition and licensing opportunities, identifying catalog gaps, and analyzing availability windows.

That is a different use case from a broadly capable assistant. Instead of relying on one general-purpose model to handle every question, Gracenote is positioning its roadmap around specialized infrastructure, reasoning, and task execution.

Jared Grusd, CEO of Gracenote, said the media industry’s AI adoption will progress from improving existing processes to redesigning how programming is managed and delivered.

“AI adoption in media will be evolutionary. Its impact will be revolutionary. Over the next few years, the industry will progress from optimizing established processes to solving challenges previously too difficult or costly to address — and ultimately to redesigning how programming is acquired, managed and delivered. Gracenote’s role is to provide the trusted content intelligence infrastructure that enables incremental advances to compound into industrywide transformation.” — Jared Grusd, CEO, Gracenote

The third layer of the roadmap is task-oriented agents. Gracenote says those agents will combine AI models, Gracenote tools, and customer-defined business logic and guardrails to automate discrete audience-facing and operational workflows.

Potential applications include content identity resolution, catalog reconciliation, feed enrichment, image selection, schedule maintenance, and availability updates. The company also outlined a rights-monitoring example in which an agent could search the open web for full episodes tied to a customer’s catalog, match them to Gracenote IDs, and flag potential violations for legal review.

Tyler Bell, Senior Vice President of AI Products at Gracenote, said production AI requires different approaches for different media problems.

“Media companies cannot capture AI’s full value with a single assistant designed to do everything. Search, catalog matching and schedule quality are fundamentally different problems, each with its own data, latency and oversight requirements. Putting AI into production requires clearly defined tasks, measurable outcomes and human checkpoints where they matter most. Gracenote’s established content foundation, deep media expertise and purpose-built tools uniquely position us to help our customers realize the transformational benefits of AI.” — Tyler Bell, Senior Vice President of AI Products, Gracenote

For sports organizations, broadcasters, and streaming services, the Sports MCP Server is the most immediate signal in the announcement. It points toward a future in which fans can find a game, understand its context, and receive relevant recommendations through AI experiences grounded in live and verified sports information.

For media operators, the broader roadmap is a bet that domain-specific data, smaller specialized models, and tightly scoped agents will be more practical than trying to solve every workflow with one large general-purpose model.

Sources

Nielsen: Gracenote Expands AI Product Roadmap With Sports MCP Server, Entertainment SLM and Agents

Gracenote Newsroom