Developing an AI Chatbot for WordPress: Enhancing User Interaction and Engagement

This project aims to create a user-friendly, AI-powered chatbot on a WordPress site to enhance user engagement and streamline information retrieval for website visitors. Leveraging an AI chatbot, the website will provide instant answers to frequently asked questions, guide users through site content, and enable a more interactive experience. Students will integrate the chatbot with WordPress, customize its interface to align with the site’s branding, and ensure it responds accurately to user queries. The chatbot will be configured to understand various user intents, providing relevant, conversational answers to commonly asked questions. It will also help users navigate the site, find content, and learn more about products or services. By the end of the project, the AI chatbot will be fully integrated and tested for usability, functionality, and performance.

Edge-Based AI Manager for Real-Time Business Analytics and Interactive User Assistance

The project involves developing a robust AI manager agent designed to operate entirely on edge computing devices, specifically targeting small to medium-sized enterprises (SMEs) in sectors like hospitality. The AI manager will collect and analyze real-time data from various applications used by SMEs—such as order management, inventory control, customer feedback, and more—to provide actionable insights, forecasts, and recommendations to improve business operations. A core component of the project is creating an interactive, conversational AI interface that allows business owners to ask questions in natural language (text or voice). The AI manager will respond by summarizing current business performance, identifying key metrics, and providing step-by-step guidance for using business applications. An essential feature of this system will be its ability to self-learn from data trends and user feedback, making it an adaptive and evolving tool tailored to each business. Students will address several challenges, including developing lightweight, efficient AI models optimized for edge processing, creating a user-friendly dashboard with visual guidance, and ensuring the AI's modular design to support integration with third-party applications. Additionally, the project will require maintaining strict data privacy and security due to the sensitive nature of business data.

Automated Edge Deployment Platform: Frontend and IAAS Integration for Distributed Application Management

This project aims to empower customers to deploy, manage, and monitor their applications directly on our custom IAAS edge infrastructure via the Shiriki Cloud frontend. The Shiriki Cloud platform will provide users with a seamless, automated experience for deploying containerized applications on distributed Balena OS-based edge devices. The IAAS infrastructure will enable resilient and autonomous edge deployment by incorporating lightweight Kubernetes (K3s), KubeEdge for edge autonomy, Ceph for distributed storage, and Cilium for secure, policy-driven networking. The project’s challenges include orchestrating large-scale application deployments across geographically dispersed edge devices, ensuring data resilience, and integrating secure communication. The frontend must provide a user-friendly interface that abstracts the complexities of edge infrastructure, allowing customers to deploy applications at scale without requiring in-depth technical knowledge.

Modular AI for Real-Time Video Analytics on Edge Devices

The objective is to develop a single, modular AI model for edge devices that can perform multiple real-time video analytics tasks, including customer flow analysis, incident detection, security monitoring, and compliance tracking, while being optimized for edge hardware and ensuring GDPR compliance. Tasks and Activities: Model Development : Build a shared backbone AI model with task-specific outputs for modular functionalities. Optimize the model for edge devices by implementing quantization and pruning techniques. Edge Integration : Develop containerized modules for dynamic task activation on Balena OS. Implement real-time processing for video analytics tasks like object detection, tracking, and incident alerts. GDPR Compliance : Integrate face-blurring and anonymization features into the model for privacy protection. Performance Testing and Optimization : Test and optimize the model across various edge hardware scenarios (e.g., single or multi-camera setups). Ensure the system supports OTA updates for easy deployment and maintenance. Deliverables: A fully integrated, modular AI model capable of performing multiple tasks on edge devices. A containerized system for easy deployment, management, and updates via Balena OS. A GDPR-compliant, real-time video processing system with dynamic task activation and resource allocation.