Project Overview
GoTeacher, a leading education technology provider, sought to automate how it tags and organizes educational materials from a variety of online sources. Manually categorizing URLs and documents by subject and grade level was inefficient, error-prone, and time-consuming. Avahi developed a powerful, AIdriven classification solution using AWS services to process and tag content in near real time. This streamlined GoTeacher’s operations and provided educators with faster access to relevant teaching resources.
About the Customer
GoTeacher operates in the education technology sector, serving schools and institutions worldwide. The company offers a robust platform for curriculum management, interactive teaching tools, and analytics, all designed to help educators optimize the learning process.
The Problem
GoTeacher needed to manage a growing volume of digital content—thousands of URLs and documents added daily—that required accurate tagging for subject matter, grade level, and topic. Manual classification was labor-intensive, often resulting in inconsistent tagging and slowed content updates. Without an automated approach, GoTeacher risked reduced educator satisfaction, inefficient retrieval of teaching materials, and missed opportunities to personalize the learning experience.
Why AWS
GoTeacher turned to AWS for its scalability, reliability, and breadth of AI/ML services. AWS solutions like Amazon Bedrock and Amazon SageMaker offered the flexibility and processing power needed to build and refine a custom classification engine capable of handling large volumes of educational content.
Why RoboArt Labs Chose Avahi
Avahi was selected for its deep expertise in AWS and proven experience in delivering AI-based cloud solutions. Its team understood how to combine AWS AI/ML services with robust serverless architectures, ensuring a seamless transition from manual tagging to automated classification. Avahi’s collaborative engagement model and track record of success gave GoTeacher confidence in a timely, cost-effective implementation that could scale with future growth.
Solution
Avahi designed and deployed an automated classification pipeline leveraging AWS Lambda, Amazon API Gateway, Amazon S3, Amazon RDS, Amazon Bedrock, and Amazon SageMaker. The process begins by ingesting URLs and documents, extracting key text elements, and applying a large language model to classify each piece of content by subject, topic, and grade level. AWS Lambda functions coordinate data flow between ingestion, transformation, and classification steps, while Amazon S3 stores both raw and
processed data. Amazon Bedrock and Amazon SageMaker power the AI models, and Amazon API Gateway provides a secure interface for accessing the classification service. Amazon RDS is used to store the final tagged data for analytics and reporting. Detailed runbooks were provided to facilitate ongoing maintenance and future enhancements.
Key Deliverables
- Automated classification system for subjects, topics, and grade levels
- End-to-end data ingestion and transformation pipeline
- AWS-based infrastructure and architecture documentation
- Runbooks for deployment and maintenance
- Analysis reports on classification accuracy and performance
Project Impact
Automating content tagging significantly reduced manual effort while improving the consistency and speed of classification. Educators now benefit from faster access to properly categorized teaching resources, enhancing lesson planning and student engagement.
Metrics:
- Verified classification accuracy against established thresholds
- Scalability to handle millions of URLs and documents
- Low latency for near real-time tagging
- Comprehensive analysis reports for continuous improvement
