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    ClassPulse AI - An Attendance Risk Evaluator

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    Alpha Data implemented the ClassPulse AI solution on AWS using a serverless, Generative and Agentic AI architecture to analyze attendance data and automate intervention workflows. ClassPulseAI Professional Services helps education organizations design, deploy, and operationalize AI-powered student engagement and learning-risk detection workflows on AWS using Amazon Bedrock, AWS Lambda, Amazon API Gateway, AWS Step Functions, Amazon S3, Amazon CloudWatch, and AWS IAM.

    Overview

    ClassPulse AI is a proactive system to monitor student attendance patterns and identify students at academic risk due to absenteeism. In the education sector, the staff often identifies issues only after students had already missed multiple classes, making intervention less effective. Institutions need a system that could automatically analyze attendance trends, detect risk patterns early, and assist academic staff in making timely intervention decisions. The solution is equipped to integrate with existing attendance systems, maintain student data privacy, and provide automated communication workflows while allowing human approval before taking critical actions. It also remains a modular system capable of being incorporated with existing attendance systems.

    Related AWS Services ClassPulseAI Professional Services is an AWS-focused implementation and enablement service for education organizations that want to build AI-powered student engagement, attendance insight, learning-risk detection, and intervention-support workflows on AWS.

    This professional services engagement relates directly to the following AWS Services:

    • Amazon Bedrock for generative AI inference and AI-generated engagement insights
    • AWS Lambda for serverless business logic and event-driven processing
    • Amazon API Gateway for secure API access to ClassPulseAI workflows
    • AWS Step Functions for workflow orchestration, review, and approval flows
    • Amazon S3 for secure storage of approved configuration files, templates, or non-sensitive content where required
    • Amazon CloudWatch for logging, monitoring, and operational visibility
    • AWS IAM for access control and least-privilege permissions
    • Amazon SNS for notification workflows where required

    Through this service, Alpha Data helps customers assess, design, configure, deploy, and operationalize the ClassPulseAI solution on AWS. The engagement may include solution architecture, AWS environment preparation, Amazon Bedrock configuration, serverless workflow deployment, integration planning, monitoring setup, documentation, knowledge transfer, and post-deployment support.

    If additional AWS infrastructure is required in the customer’s AWS account, the customer remains responsible for AWS infrastructure charges separately from the AWS Marketplace professional services transaction. Alpha Data will provide implementation documentation, IAM permission requirements, deployment guidance, and support instructions as part of the engagement.

    Highlights

    • Modernizing academic support processes without sacrificing governance or removing human oversight from critical decisions.
    • Implements AI-powered student engagement workflows using Amazon Bedrock and serverless AWS services.
    • Includes AWS architecture design, deployment guidance, monitoring setup, and operational handover with secure implementation using AWS IAM, Amazon CloudWatch, AWS Lambda, API Gateway, and Step Functions.

    Details

    Delivery method

    Deployed on AWS
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    Pricing

    Custom pricing options

    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

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    Support

    Vendor support

    Alpha Data provides implementation support during the engagement, including architecture guidance, deployment support, troubleshooting, and knowledge transfer. Customers can contact the Alpha Data cloud team through the provided support email.

    [https://www.alpha.ae/solutions/aws-services/iit-delhi-abu-dhabi-case-study/ ]