Study Cat
The Client
Trusted by more than 16 million families worldwide, Studycat is a leading language learning platform that helps children develop language skills through engaging, play-based educational experiences.
As Studycat continued expanding its language learning platform, the team sought to make business intelligence more accessible across executive, product, and growth teams. To support faster decision-making and manual workflows, Studycat engaged RevStar to develop an Agentic AI-powered analytics experience that allows internal users to explore data through natural language.
Summary
Language Learning Platform
EdTech
Amazon Bedrock, Bedrock Agents, Amazon QuickSight Q, AWS Lambda, RAG Architecture
AI Strategy, Solution Architecture, Delivery Management, AI/Data Engineering, Cloud Engineering, Quality Assurance
RevStar developed an AWS-native Agentic AI analytics platform that enables Studycat teams to ask natural language business questions, automatically generate validated queries, and receive self-service visualizations through a conversational analytics experience.
Established a scalable AI-powered analytics foundation that improves access to business insights, reduces manual reporting effort, and enables faster data-driven decision making across product, growth, and executive teams.
The Challenge
As Studycat continued growing its global language learning platform, the organization saw an opportunity to make educational and operational insights more accessible across teams. While valuable analytics data already existed within the organization, accessing meaningful insights often required technical expertise, manual query validation, and dashboard creation workflows.
Studycat wanted to establish a more intuitive analytics experience that would allow business users to explore data through natural language while maintaining accuracy, consistency, and governance. The organization also sought to build a scalable AI foundation capable of supporting increasingly sophisticated analytics, learner engagement insights, and decision-making workflows.
The Problem
Studycat needed a scalable way to transform educational and business intelligence workflows into a conversational, self-service analytics experience while maintaining data quality, accuracy, and governance.
The Solution
RevStar partnered with Studycat to design an Agentic AI-powered analytics framework capable of translating natural language questions into actionable educational and business insights.
The solution leveraged Amazon Bedrock Agents, retrieval-augmented generation (RAG), and Amazon QuickSight Q to create a multi-agent workflow that automates query generation, validation, and visualization planning. By combining conversational AI with cloud-native analytics infrastructure, Studycat established a scalable framework for self-service business intelligence, learner analytics, and future AI-powered decision support initiatives.
The Team
