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Nevine Fakhreddin@Nevine_AKF
1
Events attended
1
Submissions made
1 year of experience
About me
I develop AI systems engineered for real-world complexity, designed to deliver reliable results at scale. I transitioned into AI engineering in October 2024 with a focused and deliberate approach, building strong foundations in machine learning, deep learning, and intelligent system design, and applying that knowledge directly to real, end-to-end systems. My objective in mastering AI systems is to bridge the gap between visionary strategy and flawless execution. I have a keen interest in systems logic and technical architecture. I focus on understanding how different components are structured and integrated to create efficient workflows. My focus is on learning the underlying frameworks of technology to better understand how complex systems are built and managed, enabling me to design AI solutions that operate reliably in production environments. One of my most significant projects is AI Brand Guardian, a multi-agent cognitive analysis system developed to analyze brand perception at depth. The system reduced analysis time from weeks to minutes, significantly lowered operational cost, and maintained full transparency in how insights are generated. Rather than relying on surface-level sentiment, it models customer belief structures using coordinated AI agents. My technical work includes: - Multi-agent and agentic AI systems - Large language model integration - Retrieval-augmented generation - Machine learning pipelines and model evaluation - API-based system design and backend integration - Operational considerations including monitoring, cost efficiency, and security I'm seeking opportunities to work on production AI systems where technical architecture and system design are critical to success. I'm particularly interested in environments that tackle complex integration challenges and value deep understanding of how systems operate at scale.
🤓 Latest Submissions
AI Brand Guardian By Nevine Fakhreddin
Idea Overview: AI Brand Guardian is a specialized cognitive analysis system built for the trading industry (demonstrated on Deriv). Unlike generic sentiment tools, it reconstructs the "mental models" of traders by analyzing implicit beliefs behind their feedback. It utilizes a multi-agent architecture (CrewAI) powered by Claude 3.5 Sonnet for deep reasoning and Gemini 1.5 Flash for strategic action generation. Key Problems Solved: Early Churn Detection: Identifies switching signals (e.g., to eToro or Plus500) 30 days before they happen. Withdrawal Trust Monitoring: Detects erosion in "trust" regarding withdrawals before it leads to mass exodus. Regulatory Clarity: Monitors how traders perceive different licenses (VFSC vs. FCA). How it Works: The system orchestrates four specialized agents: Perception Agent: Scrapes trading-specific sources like ForexPeaceArmy and Reddit. Reasoning Agent: Extracts cognitive beliefs and emotional undertones (e.g., "betrayal" vs. "disappointment"). Planning Agent: Generates strategic roadmaps based on identified risks. Action Agent: Creates executable tasks for product and marketing teams. Business Impact: For platforms like Deriv, the system reduces analysis costs from weeks of manual work to just 3-4 minutes, costing approximately $0.15 per analysis while providing evidence-backed recommendations with real customer quotes.
7 Feb 2026
