Building your own AI-Powered Media Asset Management System

In today's digital landscape, managing vast libraries of media assets - from marketing videos to training materials - has become increasingly complex. Traditional file-based systems no longer suffice when teams need to quickly locate specific content based on what's happening within their media files.

This guide will walk developers through building a modern Media Asset Management (MAM) system with semantic search capabilities using Mixpeek's infrastructure.

Why Build a Modern MAM System?

Traditional MAM
- File-based storage
- Basic metadata
- Keyword search
- Manual tagging

Modern MAM with Semantic Search
- Multimodal understanding
- Automated feature extraction
- Semantic search
- Auto-organization

Consider a media production company managing thousands of video clips. In a traditional system, finding "all clips showing product demonstrations in outdoor settings" would require manual tagging and precise keyword matching. A modern MAM with semantic search can understand the content itself, making such queries natural and efficient.

Core Components

1. Feature Extraction Pipeline

The foundation of a semantic-enabled MAM is robust feature extraction. Mixpeek's pipeline can extract:

• Visual features (scenes, objects, faces)
• Audio features (speech-to-text, speaker identification)
• Text features (on-screen text, captions)
• Contextual features (scene descriptions, actions)

# Example: Configuring comprehensive feature extraction
POST /ingest/videos/url
{
  "url": "https://storage.example.com/product-demo-2024.mp4",
  "collection": "marketing-videos",
  "feature_extractors": {
    "read": {
      "enabled": true  # Extract on-screen text
    },
    "describe": {
      "enabled": true,
      "max_length": 1000  # Generate scene descriptions
    },
    "transcribe": {
      "enabled": true  # Convert speech to text
    },
    "detect": {
      "faces": {"enabled": true},
      "logos": {"enabled": true}
    }
  }
}

Feature Extraction - Mixpeek

Configure and customize multimodal feature extraction for different content types

Mixpeek

2. Intelligent Organization

Automatic Clustering

Mixpeek's clustering capabilities automatically organize content by:

• Visual similarity (e.g., grouping all outdoor scenes)
• Semantic themes (e.g., product demonstrations)
• Content type (e.g., interviews vs. b-roll)
  

Clusters - Mixpeek

Discover, organize, and search multimodal features using automatic and manual clustering

Mixpeek

Custom Taxonomies

For more controlled organization, implement custom taxonomies:

POST /entities/taxonomies
{
  "taxonomy_name": "Marketing Content",
  "nodes": [
    {
      "name": "Product Demos",
      "embedding_config": [
        {
          "embedding_model": "multimodal",
          "type": "video"
        },
        {
          "embedding_model": "text",
          "type": "text",
          "value": "Product demonstration, features showcase"
        }
      ]
    }
  ]
}

Taxonomies - Mixpeek

Create and manage hierarchical classifications for multimodal content organization

Mixpeek

3. Hybrid Search Implementation

The power of a modern MAM lies in its search capabilities. Implement hybrid search combining:

• Semantic understanding ("show me outdoor product demos")
• Visual similarity ("find scenes that look like this")
• Metadata filters (date, creator, project)

POST /features/search
{
  "collections": ["marketing-videos"],
  "queries": [
    {
      "vector_index": "multimodal",
      "value": "outdoor product demonstration with people",
      "type": "text"
    }
  ],
  "filters": {
    "AND": [
      {
        "key": "metadata.project",
        "value": "Q1-2024-Launch"
      }
    ]
  }
}

Queries - Mixpeek

Build powerful multimodal search queries across text, images, and videos

Mixpeek

Real-World Example: Video Training Platform

Consider a corporate training platform with thousands of tutorial videos. Users need to find specific techniques or concepts across multiple videos.

Challenge: "Find all demonstrations of advanced Excel pivot table techniques"

Traditional approach:

• Rely on manually added tags
• Search only video titles and descriptions
• Miss relevant content in longer videos

Modern MAM solution:

• Automatically understand video content
• Search within specific time segments
• Find relevant demonstrations regardless of video titles
• Group similar techniques automatically

# Example: Implementing semantic search for training content
POST /features/search
{
  "collections": ["training-videos"],
  "queries": [
    {
      "vector_index": "multimodal",
      "value": "excel pivot table demonstration techniques",
      "type": "text"
    }
  ],
  "group_by": {
    "field": "asset_id",
    "max_features": 5  # Return top 5 segments per video
  }
}

Best Practices and Optimization

1. Feature Extraction Strategy
   • Extract features during upload for real-time availability
   • Use appropriate models for different content types
   • Balance processing depth vs. speed
2. Search Optimization
   • Implement caching for frequent queries
   • Use pagination for large result sets
   • Tune relevance scores based on user feedback
3. Storage and Scaling
   • Implement efficient storage strategies for features
   • Use appropriate vector stores for embeddings
   • Plan for horizontal scaling

Building a modern MAM system with semantic search capabilities is now achievable using platforms like Mixpeek. The key is leveraging multimodal understanding to bridge the gap between how humans describe content and how machines process it.

Remember to:

• Start with clear use cases
• Implement comprehensive feature extraction
• Use intelligent organization through clustering and taxonomies
• Leverage hybrid search capabilities
• Optimize based on actual usage patterns

The result is a powerful system that makes finding and organizing media assets intuitive and efficient, saving valuable time for creative teams.