Automating Video Processing and Deployment Workflows for a Global Fitness Platform
Enhancing scalability, CI/CD efficiency, and media delivery using AWS event-driven architecture
30% Increase in user engagement
enabled by multilingual reach and seamless video experiences
500+ Videos processed monthly
resulting in 20% higher engagement and 15% lower buffering rates
About the Client
The client is a global leader in on-demand fitness content, delivering personalized digital workout programs accessible anytime and anywhere. The platform supports both structured training and flexible wellness experiences through a seamless, high-quality digital experience.
Industry
Wellness and Fitness
Years in business
14+ Years
Employee count
50+
Presence
USA, Europe, and Australia
“Forgeahead helped us modernize our cloud infrastructure in a way that improved scalability, reliability, and overall efficiency for our research platform.”
Dr. Michael Harrington
Chief Information Officer
the need
The client, a global on-demand fitness platform, needed a scalable and automated video infrastructure to manage growing content volumes and rising global user demand. Existing systems were not optimized for high-throughput video processing or consistent multilingual content delivery.
As the platform expanded, limitations in manual workflows, fragmented translation processes, and non-automated deployments began impacting speed, efficiency, and operational stability. The client required a serverless foundation to streamline video ingestion, processing, and distribution at scale.
Additionally, ensuring consistent multilingual support and improving global content accessibility became critical to sustaining user engagement. The platform also needed faster, more reliable deployment mechanisms across multiple applications.
the solution
Forgeahead implemented a serverless, event-driven video processing architecture using AWS Lambda, Amazon S3, Amazon MediaConvert, and Amazon DynamoDB to automate ingestion, encoding, and storage workflows at scale.
Multilingual automation was enabled using Amazon Translate integrated with Lambda and S3 triggers, allowing automatic translation of video metadata, titles, and subtitles for faster global content publishing. A standardized deployment framework was established using AWS Elastic Beanstalk and AWS CloudFormation, enabling consistent infrastructure provisioning and reducing release complexity across environments.
Content delivery was optimized through Amazon CloudFront integrated with Brightcove, ensuring low-latency playback and improved streaming performance across global regions.
Operational visibility and reliability were strengthened using Amazon CloudWatch and Amazon SNS, enabling real-time monitoring, alerts, and faster issue resolution across the platform.
The Impact
30% Increase in User Engagement
Automated multilingual workflows improved content accessibility and increased global audience interaction.
30% Faster Deployment Cycles
CI/CD automation reduced release time and improved consistency across environments.
40% Reduction in Manual Errors
Event-driven automation eliminated manual intervention in video processing and translation workflows.
20% Increase in Content Consumption
Streamlined video delivery and metadata processing improved discoverability and user engagement.
15% Reduction in Buffering Time
Optimized media delivery through CDN integration improved playback performance across regions.
Role of AWS
AWS provided the foundational serverless and event-driven architecture for automating video processing and global content delivery. AWS Lambda and Amazon S3 event triggers enabled scalable ingestion and processing workflows, while Amazon MediaConvert handled efficient video encoding and transformation. Amazon DynamoDB supported metadata storage and fast retrieval across distributed systems.
AWS also enabled global delivery, multilingual automation, and operational visibility across the platform. Amazon Translate automated content localization, while Amazon CloudFront ensured low-latency video distribution worldwide. AWS CloudWatch and Amazon SNS provided real-time monitoring and alerting, ensuring system reliability, performance tracking, and faster incident resolution.
Tech Stack
The frontend and application layer used Node.js and Python for backend services, with Brightcove supporting advanced video playback and streaming functionality across devices. This ensured a seamless and responsive user experience for large-scale video consumption.
The cloud infrastructure was built on AWS services including Amazon S3, AWS Lambda, Amazon MediaConvert, Amazon DynamoDB, Amazon RDS, Amazon CloudFront, Amazon Translate, Amazon SNS, Amazon CloudWatch, Amazon EC2, and Amazon Route 53, enabling a fully automated, scalable, and globally distributed architecture.
DevOps automation was implemented using AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, Docker, and SonarQube, ensuring secure CI/CD workflows, code quality enforcement, and consistent deployments across environments.