Solution

Every asset connected.
Every format, one graph.

Images, video, audio, documents, and structured data are not separate management problems. They are one content ecosystem. Data Graphs unifies all asset types into a single knowledge graph where relationships between content, metadata, people, and processes are queryable, governed, and accessible to both humans and AI.

Images, video, audio, documents. One graph. Full context.

The challenge

Content silos are costing you more than storage

Organizations manage content across DAMs, MAMs, PIMs, CMSs, file shares, and cloud storage. Each system handles its own format. Videos in one place. Images in another. Documents in a third. The metadata that connects them, rights information, campaign context, product associations, approval status, lives nowhere consistently.

Traditional asset management tools treat each format as its own domain. But the real value of content lies in the connections: the product a video features, the campaign it belongs to, the people who created it, the rights that govern its use, the performance data that measures its impact. Without a connective layer, these relationships are invisible.

Why Data Graphs for asset management
🎬
True multi-modal content graph
Images, video, audio, documents, and rich text are first-class citizens of the knowledge graph. Each asset carries structured metadata and links to related entities, not just file-level tags.
🔍
Context-aware search and discovery
Find content by meaning, not just keywords. Natural language search powered by GraphRAG understands your content model and surfaces assets based on relationships, metadata, and semantic similarity.
Video momentization
Link key moments in video content to structured metadata along the timeline. Goals, scenes, quotes, product appearances, and sponsor placements become queryable, clippable data points.
🛡
Content governance and compliance
Track content lifecycles, enforce approval workflows, manage rights and usage permissions, and ensure adherence to brand guidelines and regulatory standards. Full audit trails on every action.
📊
Content analytics and insights
Connect content with performance data: views, engagement, conversions. Identify high-performing assets, optimize content strategy, and measure ROI across platforms and campaigns.
🤖
AI agents over your content
AI copilots and agents access your content graph natively via MCP. Build automated workflows for tagging, classification, content recommendation, and natural language queries across your entire library.
Use cases
🏢

Unified content hub across enterprise systems

Bring together content from DAM, MAM, PIM, CMS, and other systems into a single knowledge graph. A media company unifies videos, images, articles, and metadata, transforming content findability, reuse, and cross-team collaboration. Every asset is connected to the people, products, campaigns, and rights that give it context.

🔍

Intelligent content discovery

Move beyond keyword search to relationship-aware discovery. A retailer finds product videos connected to customer reviews, social posts, and campaign performance data. Search surfaces not just the asset you asked for, but everything related to it: usage history, rights status, associated campaigns, and performance metrics.

📈

Content performance optimization

Integrate content with engagement data to understand what performs and why. A media company analyzes video and audio performance across platforms, connecting asset metadata to audience metrics. Identify which content drives engagement, which formats work best for which audiences, and where to invest next.

🛡

Governed content lifecycle management

Automate content governance from creation to retirement. A pharmaceutical company manages marketing content with approval workflows, regulatory compliance checks, brand guideline enforcement, and rights management. Every content decision is tracked, auditable, and compliant.

How it works

From scattered assets to a connected content graph

01

Model your content domain: asset types, metadata schemas, relationships, taxonomies, and governance rules

02

Ingest assets and metadata from existing DAM, MAM, PIM, CMS, and storage systems via API or bulk import

03

AI-assisted enrichment extracts metadata, tags content, and links assets to entities and relationships in the graph

04

Teams discover and query content through natural language, structured search, or graph traversal

05

AI agents connect via MCP to power automated tagging, recommendations, compliance checks, and content workflows

How Data Graphs compares

Beyond traditional DAM and MAM

Capability
Data Graphs
Traditional DAM
MAM systems
Cloud storage
Multi-modal in one graph
All formats, unified
Images + docs
Video-focused
Files only
Relationship-aware queries
Knowledge graph native
Tag-based
Metadata search
Filename/folder
AI access (MCP/GraphRAG)
Built-in
No
No
No
Video momentization
Timeline-linked metadata
No
Basic markers
No
Content governance
Full lifecycle, RBAC
Basic workflows
Partial
Permissions only
Cross-system integration
Graph-native linking
Connectors
Limited
Manual

Ready to unify your content ecosystem?

See how Data Graphs can connect every asset, format, and relationship into a single governed, AI-ready content graph.