Building a High-Performance GenAI Chatbot for Enrolment Automation Platform with AWS Bedrock

A leading enrollment automation platform revolutionizing how educational institutions attract, engage, and convert students. Serving over 1,000 institutions and processing more than 10 million student inquiries annually, it offers a comprehensive suite of solutions to streamline admissions, boost productivity, and enable data-driven decisions.

The Problem Statement

The previous chatbot faced latency issues, static data limitations, and scalability constraints. Without real-time access to updated university information, it delivered a suboptimal user experience and created operational inefficiencies.

Challenges

Response Time with OpenAI

Slow API delays caused lag in chatbot replies, frustrating users with prolonged wait times.

Data Retrieval Gaps

Missing or incomplete information due to restricted data sources, leading to unanswered user queries.

Inconsistent Knowledge Base Updates

Outdated or irregularly refreshed content made responses unreliable for real-time information needs.

Scalability Concerns

Struggled to handle increasing user requests efficiently, risking performance drops during peak usage.

Solutions

An optimized, scalable AI chatbot was deployed with real-time data fetching and automated updates, enhancing speed, accuracy, and reliability.

Moved to Claude/Sonnet on AWS Bedrock with LiteLLM

Boosted response speed and cost-efficiency while maintaining high-quality AI outputs.

Used AWS Web Crawler to fetch university data

Enabled real-time data retrieval from university websites for accurate, updated responses.

Implemented AWS Lambda triggers for crawl scheduling

Automated periodic data refreshes to maintain consistency without manual oversight.

Leveraged serverless AWS infrastructure

Ensured seamless scalability during traffic surges while minimizing operational overhead.

Outcomes

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Latency reduced by 80%, dropping from ~7 seconds to ~1.2 seconds.

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Chatbot knowledge base expanded dynamically through web crawling.

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Response accuracy improved by 35% using NLP-based semantic search.

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API costs reduced by 40% with caching and LiteLLM optimizations.

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User engagement enhanced with real-time, context-aware responses.

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