AIME brings media organisations together to research shared components for archives, trust, rights, and evaluation. Shape the work early, access the results, and reduce the risk of solving the same hard problems in isolation. Across every stream, the work differentiates on quality, cost, latency, and sovereignty.
AI is reshaping discovery and consumption. The infrastructure behind it must serve media.
Why this matters
AI agents are changing how audiences discover, consume, and trust media. Yet most media organisations are navigating fragmented archives, uncertain rights, unreliable generated output, and unclear returns on AI investment.
What should be shared infrastructure, and what should remain your differentiator? Data sovereignty is part of that question: media organisations need a real say in how AI infrastructure is designed, rather than having it dictated by the largest AI players. The same holds for monetization — whoever controls the infrastructure controls how value is captured and shared. The time to shape those answers is before the market hardens around systems that do not reflect media's needs.
The research agenda
Each stream tackles an industry-level challenge. Together, they form a practical research agenda for credible, sovereign media AI.
How can an AI agent understand a media archive, not merely search it? We are researching ways to make video, audio, image, and text collections accessible to agents while preserving provenance, permissions, rights constraints, and organisational control over data. Shared work can establish reusable patterns without requiring every organisation to expose or rebuild its collection alone.
How can existing material become new, useful formats without losing its source? This stream investigates AI-assisted transformation of archive content into personalised long-form video summaries, narrated versions, contextual explainers, and adaptable layouts. The challenge is maintaining attribution, editorial intent, rights compliance, and safeguards against unsupported claims as content moves across formats and audiences.
What happens when AI systems surface information faster than editorial teams can verify it? We are researching multi-agent architectures in which retrieval, verification, and critique work together to ground generated or AI-surfaced media before it reaches an audience. Reliable verification needs transparent evidence trails and methods tested under real media conditions.
Who should receive credit when data and content create AI value? This stream explores transparent approaches to attributing value to the archives, datasets, and rights-holders that train or ground AI systems. The answer spans technical, economic, and legal questions that no single company can resolve in isolation.
How do you know an AI system is ready to justify investment? We are building a shared evaluation environment for retrieval, generation, and agentic systems, designed around real media workflows and meaningful industry criteria. It helps organisations compare emerging approaches before committing internal resources, production data, or audience trust.
Program outputs
Pre-competitive research is designed to graduate into products that serve the media industry at large, co-owned by the organisations that participate in the AIME program.
Agentic, knowledge-graph-grounded archive retrieval productized from AIME's agentic multimodal retrieval research.
Open product pageAdditional AIME program products will be listed here as each offering is formalized.
To be announcedMembership
Join early to influence what gets built, how it is evaluated, and what it can become for your organisation.
The consortium
The initiative brings together media companies, cultural heritage institutions, researchers, and content-technology providers around the AI workload and infrastructure questions that matter to everyone.
Start the conversation
For media companies, cultural heritage institutions, and content-tech providers building the next generation of discovery, production, archives, and trust systems.