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.

Sales Process Optimization

Our company is hoping to improve our sales process to improve our ROI and overall operations. In this project, students will evaluate our current sales process, determine bottlenecks, and create recommendations as well as new training materials. Based on research and best practices you will develop new scripts for our sales staff to use to improve the quality and quantity of leads generated and increase the conversion rate. This will involve several different steps for the students, including: Evaluating our current sales process by speaking with sales representatives and examining our handbook and protocols. Researching industry sales processes and lead generation strategies. Create recommendations that will improve our leads and conversion rate. Writing new sales scripts for both phone calls and emails. Creating an updated sales process handbook.

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.