MOHAMMED DAHBANI

Agentic AI for Cybersecurity (Advanced Langchain & LangGraph) Mission 1: Design and develop an intelligent multi-agent system embodying a virtual SOC analyst, capable of autonomously handling incident tickets, while respecting the principle of human supervision. Mission 2: Develop a secure conversational agent (chatbot) for clients, available directly within the Jira ticketing tool, enabling them to interact with their incident tickets or receive personalized explanations.

KPIs and predictive maintenance of the B2B client portfolio: - In-depth analysis of equipment and historical data to identify weaknesses and opportunities for improvement. - Implementation of a predictive model with average accuracy, based on ticket history, along with a personalized dashboard displaying key performance indicators (KPIs) related to predictive maintenance. - Optimization of maintenance processes, enabling proactive planning of interventions to reduce downtime and costs. - Development of an intuitive web interface to centralize information related to predictive maintenance, as well as an RAG LLM chatbot to respond to queries about historical tickets.

The primary objective is to create a tool based on artificial intelligence (LLM RAG chatbot) that can effectively address customer inquiries. This involves offering product information to guide FairScope customers in choosing options that align with their requirements and delivering post-sales assistance through communication channels, primarily on Slack.