LLM-Based Customer Support Chatbot Development

medX Smart Solutions aims to enhance its customer support by developing a chatbot powered by Large Language Models (LLMs). The project involves setting up a chatbot that can efficiently handle customer queries using the company's existing knowledge base and troubleshooting steps. The team will explore various platforms such as Intercom and Voiceflow, as well as consider developing an in-house proof of concept. The goal is to create a solution that improves response times and customer satisfaction while reducing the workload on human support agents. This project provides an opportunity for learners to apply their knowledge in natural language processing, machine learning, and software development. Key tasks include: - Researching and evaluating different chatbot platforms. - Integrating the chosen platform with the company's knowledge base. - Developing a prototype chatbot that can handle common customer queries. - Testing and refining the chatbot for accuracy and efficiency.

CRM Integration and Streamlining for medX Smart Solutions

medX Smart Solutions is seeking to enhance its internal Customer Relationship Management (CRM) system by organizing and streamlining contact information from various sources. The current system lacks integration with platforms like LinkedIn, leading to inefficiencies in managing warm inbound leads and other contact data. The goal of this project is to create a cohesive database that consolidates contact information from LinkedIn messages, warm inbound leads, and other relevant sources. This will enable medX Smart Solutions to improve communication, enhance relationship management, and ultimately drive business growth. The project will involve analyzing existing CRM processes, identifying integration opportunities, and implementing a streamlined solution that aligns with the company's operational needs. - Analyze current CRM systems and identify areas for improvement. - Research and propose integration methods for LinkedIn and other contact sources. (Especially Analog) - Develop a plan to streamline contact management processes. Present a clearly proposed solution within the existing CRM framework for the team.

Software Iteration Planner - medX

As a Software iteration planner, you will be responsible for helping develop and implement new features for an experimental product vertical, adjusting and improving the front end of the service, preparing the system for early clinical validation settings and simulation/focus groups, and working closely with the core MedX product and design team. We would like to collaborate with students to analyze our product and devise a thorough test plan for our product (clinicassist.ai) that can be executed. This will involve several different steps for the students, including: Conducting background research on our product and analyzing user profiles, user stories, and desired outcomes. Creating a detailed testing plan for features such as mobile responsiveness, internationalization, accessibility, browser compatibility, etc. Executing the testing plan through manual means. Analyzing and reporting results of testing. Bonus steps in the process would also include: Automating tests using software to simulate user activity.

Evaluating LLMs for Medical Applications

The project aims to evaluate and benchmark various large language models (LLMs) for their effectiveness in specialized medical use cases. With the growing reliance on AI in healthcare, it is crucial to understand how different LLMs perform in terms of accuracy, efficiency, and cost-effectiveness. The focus will be on both closed-source models like Gemini, Anthropic, and OpenAI, as well as open-source models such as Llama and Mistral. The team will conduct a series of tests to compare these models, specifically monitoring token usage and associated costs. This project will provide valuable insights into which LLMs are best suited for medical applications, helping medX Smart Solutions make informed decisions about AI integration. - Evaluate the performance of different LLMs in medical contexts. - Compare closed-source and open-source models. - Monitor token usage and cost implications. - Provide recommendations based on findings.