Life
Coaching
Platform.
Modernizing a coaching platform with scalable architecture and AI-powered personalization.
Cloudicia migrated a legacy AWS stack toward a scalable microservice architecture while introducing adaptive content, predictive insights, personalized recommendations and intelligent document capabilities.
Continue developing leadership skills through the next recommended learning path.
Move beyond legacy
without losing the product.
The platform needed to move from a legacy AWS stack toward a scalable microservice architecture while continuing to support existing operations and introducing more intelligent personalization.
Legacy
AWS stack.
A scalable
foundation.
The platform became
an intelligent system.
The architecture was not treated as an isolated migration. Software architecture and AI capabilities were developed as parts of the same product direction.
Legacy platform
The existing AWS environment and operational platform formed the starting point.
Microservices
The platform was migrated toward a scalable microservice architecture.
AI layer
AI capabilities were introduced to make content and learning more adaptive.
Personalization
Recommendations and predictive insights were used to tailor the experience.
Product experience
The underlying technology became useful through the user-facing product.
Personalization is
where AI becomes useful.
AI capabilities were applied to specific parts of the experience: adapting content, surfacing insights, recommending courses and making documents easier to understand.
Adaptive content delivery
AI-powered personalization adapts content to the individual learning experience.
Predictive user insights
The platform uses AI to surface insights around user behaviour and progress.
AI Coach Roadmap
An AI-powered roadmap supports skill development through a more personalized journey.
Recommended courses
Courses are recommended based on the user's context and learning needs.
Document intelligence
Documents can be summarized and queried through an intelligent document experience.
Architecture and AI
working toward the same outcome.
AI is most useful when it is connected to a real product problem.
Here, that meant making learning more adaptive without losing the foundation of the product itself.