AI in DAM starts with finding content
A conventional search works well when filenames, keywords, or other metadata match the query. Semantic search goes further: it uses vector representations to identify content by meaning and visual similarity. A query such as “modern kitchen with dark wood” can therefore find suitable images even when that exact wording is absent from the metadata.
Agravity GlobalDAM combines semantic vector search with text-based retrieval in a hybrid search. Exact terms, structured metadata, and semantic similarity complement one another. This is especially useful in large, heterogeneous media collections: known product numbers remain precisely searchable, while natural-language descriptions provide additional ways to discover content.
AI tagging for images, videos, and documents
Agravity GlobalDAM can analyze images, videos, and documents and generate suggestions for keywords, descriptions, and other metadata. These suggestions reduce manual preparation and help create more consistent collections. One practical use case is preparing alternative text for images automatically. The responsible team retains final editing and approval.
AI-assisted translation helps prepare metadata for multiple languages. Terms and descriptions do not need to be recreated from scratch for every market. People can then review and adapt specialist terminology, brand language, and context.
Making video content searchable
Video analysis recognizes content within a video and makes it accessible through metadata. Teams can identify the scenes, objects, or topics a video contains instead of describing only the file as a whole.
For faces that have previously been trained or registered, Agravity GlobalDAM also supports face recognition in videos. This can simplify research for appearances of specific people in an approved media collection. Such capabilities require clear permissions, defined purposes, and a data-protection-compliant organizational basis.
Data protection and security for AI capabilities
AI-assisted processing must not cause organizations to lose control over their content. Agravity GlobalDAM therefore operates its AI capabilities within Microsoft Azure infrastructure and configures them for processing in European data regions. Media, metadata, and generated analysis results are encrypted in transit and at rest and remain subject to the roles and permissions defined in Agravity.
Inputs, outputs, and embeddings processed by the Azure AI services used by Agravity are not accessible to other customers or external model providers. They are not used to train general-purpose AI foundation models without explicit permission.
Data protection nevertheless remains a shared organizational and technical responsibility. Particularly when processing personal content or recognizing trained faces, organizations must define the purpose, access rights, retention, and required legal basis. Agravity supports these controlled processes through permissions, traceable metadata, and its human-in-the-loop principle.
AI assistants for knowledge and new access paths
An AI chatbot for the Admin Guide helps administrators access product knowledge and instructions. A corresponding assistant for the User Guide is planned. These assistants are intended to answer questions about working with Agravity GlobalDAM in natural language and make documentation easier to access.
Alongside its API-first architecture, Agravity is developing its own MCP server. The Model Context Protocol is intended to give AI applications and agents standardized, controlled access to approved functions and information. The MCP server is currently in development.
C2PA support for provenance and labeling
Agravity plans full C2PA support in GlobalDAM before Christmas 2026. The planned scope includes reading existing C2PA information, mapping it to metadata, visibly labeling content, and signing relevant assets. C2PA provides a technical standard for documenting provenance and editing steps in a machine-readable form.
These capabilities can support transparent content processes and the implementation of relevant EU AI Act requirements. The labeling legally required in an individual case still depends on the content, its use, and the publication process.
Human in the loop remains the guiding principle
All AI capabilities in Agravity GlobalDAM are designed for human-in-the-loop processes. AI generates suggestions, accelerates research, and prepares information. People can review, correct, and approve the results. This matters particularly for metadata: automation saves time, while reliable data quality comes from combining machine support, subject-matter context, and clear responsibility.
Agravity GlobalDAM AI capabilities at a glance
- Available: semantic vector search and hybrid search.
- Available: AI tagging and metadata suggestions for images, videos, and documents.
- Available: video content recognition and recognition of trained faces in videos.
- Available: AI-assisted translation.
- Available: AI chatbot for the Admin Guide.
- Planned: AI chatbot for the User Guide.
- Planned before Christmas 2026: reading C2PA data, mapping metadata, visibly labeling content, and signing assets.
- In development: an MCP server complementing the API-first architecture.
- Consistent principle: human in the loop to protect data quality.
Agravity GlobalDAM treats AI as an integrated part of a modern DAM: enabling better discovery, more efficient metadata work, searchable video content, and controlled, traceable content processes.