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Gen AI & Meta Data Extraction for Media Automation Platform
Media & Entertainment

Gen AI & Meta Data Extraction for Media Automation Platform

Automating media content analysis, meta data extraction, and workflow optimization with Gen AI and serverless architecture.

Client

Media & Entertainment Company

AWS CDK
Next.js 15
TypeScript
Amazon Bedrock
AWS Lambda
DynamoDB
S3
React 19
Tailwind CSS
API Gateway

The Challenge

Our client, a media and entertainment company, needed to transform their manual content processing workflows into automated, scalable solutions. Media professionals were spending countless hours on repetitive tasks including content analysis, metadata extraction, quality assessment, and compliance reviews. They needed a comprehensive automation platform that would address several critical requirements:

  • Automated content analysis across multiple media types
  • Real-time processing with rapid turnaround for time-sensitive content
  • Integration with industry databases and third-party services
  • Scalable serverless architecture to handle variable workloads
  • Cost optimization through pay-per-use infrastructure
  • User-friendly interface for content upload and workflow management

Our Solution

We designed and implemented a comprehensive media automation platform combining a modern React frontend with a fully serverless AWS backend that automatically processes and analyzes various types of media content using Gen AI-powered workflows and advanced meta data extraction.

Architecture

  • Frontend Application: Next.js 15 with React 19 for high-performance user interface
  • Serverless Backend: Event-driven AWS Lambda functions for content processing
  • AI-Powered Analysis: Multiple specialized AI models for different content types and use cases
  • Infrastructure as Code: AWS CDK with TypeScript for reproducible deployments
  • Database Integration: PostgreSQL RDS with industry-specific API integrations
  • Content Storage: Segregated S3 buckets for media files and configuration with lifecycle policies
  • API Management: AWS API Gateway for secure REST API endpoints
  • Secrets Management: AWS Secrets Manager for API keys and database credentials

Development Process

  • Modern Frontend Stack: Next.js 15 with App Router and Server Components
  • Type-safe Development: Full TypeScript implementation across frontend and backend
  • Component Architecture: Reusable React components with custom hooks
  • Responsive Design: Tailwind CSS v4 with dark/light theme support
  • Multi-format Processing: Support for various media formats (PDF, video, audio, text)
  • Workflow Orchestration: Dynamic Lambda function chains for complex automation tasks
  • AI Configuration: Versioned prompts and models stored in S3 for flexible automation
  • Multi-environment Support: Separate dev, staging, and production configurations

Key Features

  1. Intelligent Content Analysis: AI-powered processing for subtitles, metadata, and reviews
  2. Industry Integration: Connect with entertainment databases and third-party services
  3. Real-time Processing: Immediate results with progress tracking for all workflows
  4. Comprehensive Reporting: Detailed analytics with industry insights and recommendations
  5. Professional UI/UX: Media industry-optimized interface with intuitive workflow design
  6. Multi-format Support: Automatic validation and processing of various media formats
  7. Error Handling: Comprehensive error management with user-friendly messaging
  8. Performance Metrics: Built-in monitoring and optimization for all automation tasks

Technical Implementation

  • AWS Lambda Functions: Specialized functions for different automation workflows
  • Amazon Bedrock Integration: Claude 4 models for advanced content understanding
  • DynamoDB Tables: Content metadata storage and processing history tracking
  • CloudFront Distribution: Global content delivery for frontend assets
  • ECS Fargate: Containerized frontend deployment with auto-scaling
  • Custom Lambda Layers: Shared dependencies and utilities
  • Batch Processing: Support for multiple concurrent content processing tasks

Expected Outcomes

The solution is designed to transform media content operations:

  • Processing Speed: Significantly faster content processing workflows
  • Scalability: Capability to handle variable workloads efficiently
  • Cost Efficiency: Optimized operational costs through serverless architecture
  • Accuracy: Consistent, automated analysis using AI models
  • Workflow Optimization: Substantial reduction in manual tasks
  • User Experience: Streamlined interface for media professionals
  • Business Value: Enhanced operational efficiency and productivity