EMAM launches five-week AssetMind AI pilot for media teams

EMAM has launched a collaborative pilot program for AssetMind, its media AI platform, to let organizations test accuracy, workflows, infrastructure needs and costs using their own media. The five-week program is aimed at broadcasters, media groups, sports networks, government agencies and corporate teams ahead of a planned 2027 production release.

Why it matters: - Media organizations are trying to add AI without giving up control of content, infrastructure or security policies. - The pilot is meant to show whether AssetMind fits real workflows before a production deployment. - The program is aimed at organizations that need to compare AI results and costs against existing tools or manual work.

What happened: - EMAM, Inc. launched the AssetMind Collaborative AI Pilot Program on Nov. 2, 2026. - The structured, five-week program pairs customers with EMAM's applied AI team. - Participants evaluate AssetMind using their own media and workflows. - The pilot can run on premises or in the cloud. - The target users include broadcasters, media organizations, sports networks, government agencies, corporate media teams and technology partners.

The details: - The pilot helps customers assess AI accuracy, workflow fit, infrastructure requirements and costs. - Participants and EMAM define use cases and acceptance criteria, then review baseline results and retest after optimization. - The evaluation factors in the customer's content, languages, processing capacity and costs. - Customers provide representative media and subject-matter experts to score results. - EMAM deploys and operates the pilot environment, tunes the platform and supports a production readiness assessment. - The customer decides whether the results meet organizational needs. - The cloud option uses an Amazon EC2 deployment so teams can evaluate AI output and user experience without an initial GPU hardware purchase. - The on-premises option uses customer- or partner-supplied approved NVIDIA GB10, GB300 or RTX PRO 4500-class hardware. - The on-premises path is designed to test private, sovereign or air-gapped production deployments. - Organizations can request a pilot discussion by emailing pilot@mediaassetmind.com. - AssetMind supports on-premises and air-gapped deployments with distributed processing through AssetMind nodes. - AssetMind capabilities available for evaluation include transcription, multilingual translation, automatic titles, summaries, metadata and tags. - The platform also includes semantic search with text and visual embeddings, visual scene and object understanding, face detection, face clustering and person identification. - Additional features include media ingest, indexing, AI-assisted workflow automation, web-based editorial workflows, Model Context Protocol connectivity, and archiving to NAS and local or S3-compatible storage with media restoration and transfer. - AssetMind uses fixed software pricing to support predictable software costs. - If standard AssetMind models miss the agreed quality target, EMAM may evaluate specialized third-party models or partners after a joint review of quality, security, licensing and incremental costs. - AssetMind was previewed at IBC 2026 in Amsterdam, and EMAM plans a production release in 2027.

Between the lines: - The pilot design signals a sales process built around proof, not a broad demo. - By testing with customer media and customer scoring, EMAM is positioning AssetMind for workflows where accuracy and governance matter as much as feature depth. - The option to swap in third-party models suggests EMAM expects some deployments to need customization beyond default models.

What's next: - Interested organizations can ask for a pilot discussion now. - EMAM is steering AssetMind toward production deployment planning after pilot evaluation. - A broader release is planned for 2027.

The bottom line: - EMAM is packaging AssetMind as a test-before-buy AI platform for media teams that want local control, measurable output and a clearer path to production.