As Fiction Express continued to scale, the ability to access data quickly and independently became critical. The company needed a modern analytics foundation that could support day-to-day decision-making today, while preparing the business for future AI and generative AI capabilities.
Fiction Express is an EdTech platform that connects students and educators through interactive digital reading experiences. The platform helps schools improve literacy, engagement, and learning outcomes through data-driven insights.
Industry: EdTech / Digital Education
As Fiction Express continued to scale, the ability to access data quickly and independently became critical. The company needed a modern analytics foundation that could support day-to-day decision-making today, while preparing the business for future AI and generative AI capabilities.
Fiction Express had valuable data across the business, but it was spread across multiple disconnected systems. Extracting insights required technical expertise, reporting processes were fragmented, and business users had limited ability to access the information they needed independently.
As the organization grew, this created a widening gap between the data Fiction Express had and the insights teams could act on. Requests often depended on technical teams, slowing down reporting cycles and limiting the frequency of analysis.
Beyond the immediate operational challenge, Fiction Express also needed to prepare for what came next. The existing setup was not built to support future AI and generative AI use cases, making it difficult to turn data into a scalable foundation for smarter, more automated capabilities.
CloudZone designed and implemented a scalable, GenAI-ready analytics platform on AWS, giving Fiction Express a centralized foundation for data access, querying, and visualization.
The solution created a unified data layer that enables both technical and business users to work with data more independently. It was designed to meet Fiction Express’s current analytics needs while supporting future AI use cases without requiring major rework.
The architecture included:
• Centralized data ingestion into Amazon S3
• Flexible querying using Amazon Athena
• Visual analytics through Amazon QuickSight
• Access through familiar tools such as Excel
• A clean, scalable data foundation ready for future GenAI initiatives
By combining AWS-native services with a practical data architecture, CloudZone helped Fiction Express move from fragmented reporting to a reliable, self-service analytics environment.
Fiction Express now operates a reliable, self-service analytics platform that gives teams faster, easier access to the data they need.
The new platform improved access, efficiency, and adoption across the organization, while creating a strong foundation for future AI expansion.
Key outcomes include:
