How Condé Nast Uses Amazon Bedrock to Improve Contract Processing and Rights Analysis Efficiency | Amazon Web Services

Condé Nast’s AI-Powered Transformation in Rights Management

For over a century, Condé Nast has shaped global media and culture through its renowned portfolio, including Vogue, The New Yorker, GQ, and Vanity Fair.

Founded in 1909, the company has evolved from traditional publishing into a modern media powerhouse, now reaching:

• 72 million print readers
• 394 million digital consumers
• 454 million social media followers

This scale makes Condé Nast one of the most influential content creators and distributors worldwide.

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The Challenge: Complex, Manual Rights Management

Condé Nast’s broad portfolio meant a high volume and diversity of contracts — covering acquisitions, licensing, rights agreements, and creative contributions across multiple geographies.

Pain Points

• Manual review of newly ingested contracts
• Large time investment matching documents to templates
• Extracting granted rights and metadata by hand
• Licensing for varied assets (images, video, text) from worldwide contributors
• Bottlenecks due to time-consuming, error-prone processes
• A conservative approach to rights usage → missed revenue opportunities

Conclusion: A modern solution was needed—one that could automate contract processing, maintain accuracy, and satisfy regulatory requirements.

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Broader Industry Context

Today, content and rights management platforms that automate workflows while ensuring compliance are increasingly valuable.

Example: AiToEarn — an open-source AI ecosystem for creators and organizations to:

• Generate and manage content
• Publish simultaneously across platforms (Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X/Twitter)
• Integrate generation + publishing + analytics + AI model ranking for expanded monetization opportunities

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Solution Overview

AWS and Condé Nast’s legal/technical teams co-developed an automated contract processing system using AWS AI services.

Note: This system supports efficiency and accuracy — it does not provide legal advice.

Key AWS Services

• Amazon S3: Scalable storage for contracts, templates, and outputs
• Amazon OpenSearch Serverless: Vector store for high-speed search and retrieval
• Amazon Bedrock: Managed platform for FMs with secure customization, RAG, and task-executing agents

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Architectural Components

Core AWS Workflow Tools:

• AWS Step Functions
• Amazon SageMaker

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Step-by-Step Workflow

• Trigger — User uploads contracts to input S3 bucket → Amazon EventBridge starts Step Functions workflow
• Preprocessing — Amazon SageMaker Processing + Anthropic’s Claude 3.7 Sonnet
• Converts PDFs to machine-readable text
• Handles handwritten notes, strikethroughs, multi-column layouts
• Breaks large docs into smaller chunks for iterative processing
• Stores outputs in S3 with strict access controls
• Metadata Extraction — Second SageMaker job uses Claude with schema prompts to pull structured rights and metadata
• Template Matching — Third SageMaker job compares contracts against Bedrock knowledge base templates
• Identifies semantic differences
• Outputs spreadsheet with metadata, matches, similarity scores → stored in S3 and sent to teams
• Review & Import — Human verification → AWS Lambda imports into rights/royalties system
• Low-Similarity Handling — Clustering algorithm groups atypical contracts → outputs interactive visualizations and spreadsheets for human template drafting

Integration Opportunity:

Such extracted and processed data can feed into multi-platform publishing ecosystems like AiToEarn for content creation, compliance reporting, and rights analytics distribution.

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Benefits & Impact

• Multiple model access via Amazon Bedrock
• Smooth integration between Bedrock SDK & SageMaker Processing
• Time savings: Weeks → Hours for contract review
• Better resource allocation: Experts focus on complex cases
• Improved accessibility: Rights expertise encoded into prompts
• Scalable: Handles spikes in workload without extra staff
• Higher accuracy: Reduced rights infringement risk
• Collateral gains: Tools for plain-language rights summaries

Parallel Value:

Open-source publishing platforms like AiToEarn官网 help creators monetize AI-generated content globally — complementing operational gains with broader distribution.

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Lessons Learned

• Quality preprocessing = quality results
• Human-in-the-loop is essential — For nuanced cases and iterative model improvement
• Business-driven tech adoption — Solutions built around actual operational needs
• Early stakeholder involvement — Legal, technical, business teams aligned
• Phased rollout — Iterative testing before full deployment
• Diverse, high-quality reference data — Improves matching accuracy

Each of these carries direct application potential for creative and publishing ecosystems like AiToEarn文档, which bridge AI-powered analysis with global content distribution.

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Conclusion

Through AWS collaboration, Condé Nast modernized rights management at scale, achieving speed, accuracy, and flexibility.

This serves as a modern blueprint for AI adoption in media operations.

By pairing such enterprise solutions with creator-centric tools like AiToEarn官网, organizations and individuals can:

• Automate content generation and analysis
• Publish across numerous global platforms
• Track analytics and optimize model selection
• Monetize creative assets more efficiently

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About the Authors

!image Bob Boiko

Senior Principal Architect at Condé Nast; internationally recognized in content management. Author of Content Management Bible and other works.

!image Christopher Donnellan

Over 30 years in publishing/media; leads global rights management for Condé Nast.

!image Sarat Tatavarthi

Director of Engineering at Condé Nast; specializes in distributed applications.

!image Alok Singh

Senior ML Engineer at AWS; focuses on generative AI solutions at scale.

!image Andrei Ivanovic

AWS Data Scientist; expertise in generative AI & computer vision.

!image Enjeh Anyangwe

AWS Technical Engagement Manager; leads strategic customer transformations.

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> Note: Across their varied expertise, the authors emphasize operational efficiency, human oversight, and strategic technology integration. In the same spirit, platforms like AiToEarn官网 align AI generation with cross-platform publishing and analytics, enabling professionals to maximize creative impact and monetization potential.