UMM Digital, a leading global digital marketing agency supporting multiple international brands, partnered with RootQuotient to implement a Generative AI–powered Marketing Content Generation System hosted on AWS. The goal was to accelerate content production, increase brand consistency, and reduce operational workload across campaign teams.

RootQuotient designed and deployed a scalable AWS-based GenAI solution that automates long-form and short-form content creation, integrates brand-specific knowledge, and enforces consistent tone and compliance across all output.

Background

UMM Digital operates across multiple regions delivering creative, digital strategy, and marketing services for large enterprises and consumer brands. Their teams manage high-volume content needs across social media, websites, ads, email, and editorial formats.

As campaign workload increased, UMM Digital sought a scalable, automated solution to maintain quality while increasing content throughput.

Challenges Faced by Customer: 

• Manual, Time-Consuming Production: Each campaign asset required 3–5 hours to produce due to manual copywriting, content review, and approval cycles.
• Scaling Personalization: Delivering tailored messaging for each client and content type (social, blog, whitepaper) lacked a unified workflow and common standards.
• Brand Consistency: Variations in voice, tone, and structure since different teams worked on assets without a centralized content framework.
• Inefficient Asset Reuse: Each brief started from scratch, there was no system to modularize content, leading to duplication of effort.
• Operational Inefficiency: Disjointed tools and processes forced between teams, slowing campaign launches and introducing errors.

UMM Digital required a production-grade GenAI system integrated with AWS to automate content production while preserving quality and governance.

Objective: 

• Automate Content Generation: Shorten asset creation time from hours to minutes through AI-driven content and image automation.
• Standardize Brand Messaging: Ensure every asset (across campaigns, teams, and content types) reflects a unified brand voice and structure.
• Enable Personalization at Scale: Use prompt engineering and dynamic templates so content can be tailored for different industries and tones, automatically.
• Optimize for Efficiency and Cost: Adopt a cloud-native, serverless architecture to eliminate idle resource costs and scale only as per workload.
• Reduce Manual Workload: Free up skilled team members from repetitive content creation tasks to focus on strategy, ideation, and campaign optimization.

Rationale: 

• AWS-Native and Serverless: Using Amazon S3, Lambda, API Gateway, and GenAI services enabled an auto-scaling, fully managed workflow with minimal operational overhead.
• GenAI Advantage: Bedrock’s Claude 4 Sonnet and Titan Image Generator V2 offered cutting-edge AI for both creative writing and visuals, accelerating throughput while preserving quality.
• Security and Compliance: AWS provided robust tools for access control, encryption, logging, and compliance, essential for serving regulated clients.
• Flexibility and Future-Proofing: Modular design (using S3 buckets for templates, brand kits, and generated assets) and prompt engineering made it simple to update, personalize, or integrate with other tools as needs grew.
• Rapid Time-to-Value: This architecture allowed for agile development and iteration, realizing business benefits quickly and providing a clear roadmap for scaling as demand increased.

Solution Delivered by RootQuotient

RootQuotient built and deployed a Generative AI Marketing Content Generation System for UMM Digital using Amazon Bedrock, AWS serverless compute, and a retrieval-augmented generation (RAG) architecture.

Key Capabilities Implemented

1. Automated Multi-Channel Content Generation

The system generates SEO-optimized blogs, emails, ad copy, and social posts using configurable templates aligned to each brand.

2. Amazon Bedrock Model Orchestration

UMM Digital’s platform integrates multiple foundation models via Bedrock:

• Claude for creative narrative-style content
• Titan for structured content patterns
• Llama for summarization and refinement tasks

The orchestrator automatically selects the optimal model based on content type.

3. RAG Layer for Brand & Knowledge Integration

A semantic retrieval layer built on:

• Amazon OpenSearch Serverless

This retrieves:

• Brand guidelines
• Past campaigns
• Product descriptions
• Styling rules

This ensures all generated content aligns with brand identity and factual context.

4. Governance & Quality Guardrails

AWS Lambda–based validators check:

• Tone alignment
• Factual correctness
• Sensitive content
• SEO scoring
• Formatting & readability

Any content failing thresholds is auto-revised.

5. Scalable Serverless Architecture

Built using:

• AWS Lambda
• Amazon API Gateway
• AWS Step Functions
• Amazon S3
• Amazon CloudWatch
• AWS IAM

This ensures cost-optimized, highly available operation across UMM Digital’s global teams.

Business Outcomes for UMM Digital

Within the first few weeks of production use, UMM Digital reported measurable improvements:

📈 65% reduction in content production time

Automated workflows drastically reduced manual drafting effort.

📈 40% increase in content output capacity

Teams handled more client campaigns without additional headcount.

📉 90% reduction in manual editing cycles

Guardrails improved brand consistency and factual correctness.

⚡ 80% faster campaign turnaround

Real-time content generation enabled rapid experimentation and execution.

🌍 Improved brand consistency across markets

RAG ensured stable messaging across all content types.

The system is actively used across multiple UMM Digital client engagements and regions.

Customer Feedback

“The Generative AI platform built by RootQuotient has significantly transformed our content operations. We can now deliver high-quality campaigns faster and at greater scale, with consistent brand voice across all clients.”
— Head of Marketing Technology, UMM Digital

AWS Services Used

This customer implementation leverages a comprehensive AWS GenAI stack:

• Amazon Bedrock – Foundation model access and orchestration
• Amazon OpenSearch Serverless – Retrieval-augmented generation layer
• AWS Lambda – Guardrails, transformations, evaluation logic
• AWS Step Functions – Workflow orchestration
• Amazon API Gateway – Secure system access
• Amazon S3 – Content storage and versioning
• Amazon CloudWatch – Monitoring and automated alerts
• AWS IAM – Access control and security governance