Building Develomers Inc | Cracked Developer with passion for Product Design & AI

Harsh Parekh  • Peerlist

Events and expos are a valuable way for businesses to connect with potential customers and partners, showcase their products and services, and gain industry insights. However, capturing visitor information has been a long-standing challenge for exhibitors, especially at large events with many visitors. Traditionally, booth owners would ask visitors for their business cards, write down their details in a visitor book, or rely on the limited availability of NFC contact sharing. These methods are time-consuming and inefficient and prone to human error, losing data, and inconsistent follow-up. This is where ExpoIn comes in. ExpoIn is an innovative mobile app that solves the problem of inefficient and time-consuming for visitor data collection at events. ExpoIn also uses QR codes to capture visitor information. By scanning the QR code, visitors can share their contact details with exhibitors, eliminating the need for physical business cards or manual data entry. ExpoIn generates a unique QR code for each visitor, which can be printed on their badge or business card. When the exhibitor scans the QR code using the ExpoIn app, the visitor's contact details are automatically captured and stored in the lead list. This feature is convenient for exhibitors and visitors, as they do not have to carry physical business cards or worry about losing them. ExpoIn also allows exhibitors to customize the lead form, which is the form that visitors fill out when they scan their QR code. Exhibitors can check out all the information from visitors, such as their names, email addresses, phone number, and websites. By customizing the lead form, exhibitors can collect the information that is most relevant to their business and follow up with potential customers more effectively

ExpoIn

1. Trained on scientific documents for a comprehensive understanding of technical language and context. 2. Utilizes the RAG model for effective retrieval and generation on top of pre-trained Large Language Models, ensuring accurate and contextually relevant outputs. 3. Employs NextJS and ShadCN Library for seamless processing of scientific documents, enhancing user experience. 4. Versatile compatibility with various file formats (pptx, docx, pdf, latex, csv, epub, ipynb, json) and the ability to handle multiple files. 5. Technology Used: Python, Large Language Models, llama-index, open-ai, NextJs, Flask, and Firebase