Frontier ConnectIQ

Frontier_ConnectIQ User Interface

Inspiration

Frontier Technology

What it does

Frontier ConnectIQ is a web application that optimizes users' online experiences by proposing Frontier products based on their unique requirements. Users provide important information such as network speed, download speed, number of connected devices, and network strength. The program gives detailed instructions on how to find and enter these values. Based on this information, it proposes appropriate products to improve connectivity or performance. Furthermore, an AI-powered chatbot helps users with queries, issues, and additional information regarding the recommendations.

How we built it

Frontend: Created with Python's Streamlit for a sleek and responsive web interface. Streamlit's simplicity facilitated rapid development and dynamic updates based on user inputs.

Backend: Developed in Python with SambaNova Cloud API and Streamlit for smooth integration of recommendation system and AI chatbot. This design allowed for quick data handling and real-time answers.

AI Chatbot: Implemented using Meta Llama API powered by SambaNova Cloud for natural language processing, allowing the chatbot to handle user queries effectively.

Challenges we ran into

Recommendation System: Creating an algorithm that matches user inputs to relevant products while remaining personalized and valuable.
AI Chatbot Training involves tuning the chatbot to understand technical queries and offer accurate, context-aware answers.
Integration of Recommendation system and AI Chatbot
Time constraints involve balancing feature complexity with project deadlines during development.

Accomplishments that we're proud of

Successfully created a working, user-friendly online application that connects user demands with product recommendations.
Implemented an AI chatbot to simplify troubleshooting and improve user experience using SambaNova Cloud API.
Delivered a solution that aligns with Frontier's objective to develop Gigabit America through personalization and innovation while also utilizing SambaNova.

What we learned

Enhanced our understanding of creating recommendation systems and integrating them with real-world applications.
Improved skills in designing AI chatbots and training them to handle diverse user inputs.
Learned how to prioritize user experience and accessibility when developing tech-heavy solutions.
Gained valuable insights into effective time management and collaboration in a fast-paced project.

What's next for Frontier ConnectIQ

Enhanced Data Automation: Implement features to automatically detect user parameters for a seamless experience.
AR Integration: Allow users to visualize how new products, like routers, would fit into their homes.
Expanded Product Recommendations: Incorporate additional categories like entertainment bundles or smart home devices.
Multilingual Support: Expand the AI chatbot to support multiple languages for better accessibility.
User Feedback Loop: Introduce a feedback mechanism to refine product recommendations and improve accuracy.
Mobile App Version: Develop a mobile-friendly version to reach a broader audience