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How Addly AI Scaled Content Generation and Decreased Latency on Amazon Bedrock

Solutions for marketing teams, digital agencies, and enterprise content creators.

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Addlly AI is a Singapore-based generative AI platform that provides groundbreaking solutions for marketing teams, digital agencies, and also enterprise content creators. Their platform improves content production efficiency by simplifying SEO-optimised blog creation, social media management, and brand-consistent marketing workflows while ensuring high performance in the digital marketing sector.

Industry

AI Agents for GEO and SEO

Services

Amazon Bedrock, Bedrock Agents, AWS Lambda, Amazon S3, Amazon CloudWatch, AWS Identity and Access Management (IAM)

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Business Requirements

Addlly AI required a resilient, adjustable, and high-performance infrastructure to support its swiftly growing generative AI content platform. In fact, the system needed to seamlessly handle high-volume API requests for content generation, ensuring low-latency responses even during peak marketing campaign cycles, as well as, global traffic spikes. Achieving 99.9% uptime was indeed a critical requirement, thus, necessitating a managed, serverless architecture that could eliminate the bottlenecks of traditional manual prompt engineering alongside model orchestration.



Security and brand integrity were key priorities, given that the platform processes sensitive proprietary brand data and customer information. The infrastructure had to safeguard data handling and regulatory compliance by following enterprise standards, necessitating end-to-end stringent encryption and isolated model environments. Besides, automation was utmost important for streamlining the content production pipeline, thus, enabling "zero-prompt" workflows that maintain consistent brand voice across diverse marketing channels without the need for manual intervention.

To balance cost and performance, Addlly AI needed a flexible, pay-as-you-go model that optimized compute resources based on real-time demand. The architecture required intelligent model selection and auto-scaling capabilities provided by Amazon Bedrock, allowing for cost-efficient operations while maintaining superior generation quality. Furthermore, the solution had to provide real-time observability along with performance metrics to support consistent optimisation of AI agent workflows alongside search engine visibility outcomes.

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The Challenge

Orchestrating Multiple LLM Workflows: Managing a multi-layered generative AI infrastructure required uniform coordination between various Large Language Models (LLMs) and brand-specific data sources. The challenge was to withdraw from a fragmented setup of manual prompt engineering and create a unified system that could maintain consistency in brand voice and content quality across diverse marketing channels.

Security and Brand Data Privacy: Safeguarding sensitive customer brand data, as well as proprietary marketing assets was a top priority. Implementing a secure yet enterprise-grade AI environment that ensured data privacy while maintaining uninterrupted connectivity between model APIs and content databases was essential to meet the trust requirements of global clients.

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The Solution

Centralized Model Orchestration with Amazon Bedrock

A unified AI architecture was implemented using Amazon Bedrock, allowing Addlly AI to access and manage Anthropic Claude models through a single API.This eliminated the complexity of managing multiple model providers and integrated seamlessly with the existing AWS environment.

Automated “Zero-Prompt” Workflows

By leveraging Bedrock Agents, the solution implemented sophisticated prompt orchestration that automatically crafts content based on brand-specific data.This “zero-prompt” approach allows users to generate SEO-optimized blogs and marketing assets without manual engineering, ensuring consistent brand voice and high-quality output every time.

Serverless Scalability with AWS Lambda

AWS Lambda and Amazon Bedrock’s serverless architecture were leveraged to handle dynamic computational demands.This approach ensures that the platform scales automatically to support high-volume content generation requests during peak marketing cycles while maintaining low-latency performance without the need to manage underlying servers.

Brand-Specific Data Grounding

Amazon S3 was utilized as a secure data lake for storing customer brand guidelines, website crawls, and historical content.This data is fed into the AI agents to provide context-aware outputs, ensuring that all generated content is grounded in factual, brand-specific information for superior SEO and GEO results.

Secure and Compliant AI Environment

AWS Key Management Service (KMS) and AWS IAM were implemented to ensure that all brand data and model interactions are encrypted and access-controlled.The solution ensures that customer data remains within the Addlly AI VPC and is never used to train base foundation models, maintaining the highest standards of data privacy and security.

Real-Time Observability and Performance Tracking

Amazon CloudWatch was integrated to provide real-time monitoring of API response times, model invocation counts, and error rates.This observability enables Addlly AI to proactively optimise their AI agents, ensuring a 99.9% platform uptime and maintaining the 40% reduction in latency achieved through the migration.

Result

The new Bedrock-powered clinical review engine delivered immediate improvements:

Scalability and High Performance The transition to a serverless, managed architecture on Amazon Bedrock enabled Addlly AI to scale reliably during high-volume content generation cycles. The platform now supports high-concurrency API requests with 99.9% uptime and delivers real-time AI outputs with a 40% reduction in latency.
Improved Security and Brand Integrity AWS-native encryption and isolated data environments ensured that proprietary brand data remained secure and was never used for external model training. These controls helped Addlly AI meet the strict compliance standards required by global marketing organizations.
Operational Efficiency Through Automation By implementing zero-prompt automated workflows and Bedrock Agents, Addlly AI increased overall content production efficiency by more than 60%. This significantly reduced manual prompt engineering efforts and allowed teams to focus on innovation and AI agent expansion.
Cost-Optimised AI Infrastructure Using the serverless, pay-as-you-go pricing model of Amazon Bedrock and AWS Lambda, Addlly AI eliminated the need for always-on GPU instances. Dynamic resource allocation and efficient data storage in Amazon S3 resulted in a cost-efficient platform that scales linearly with user growth.
Results visualization showing target and metrics

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