Project SENTINEL | 11 Key Client Clarification Questions Before Starting an AI Project

Before starting an AI project, understanding the client’s requirements is just as important as writing code. In this video, I present **11 key clarification areas for Project SENTINEL**, a fraud detection project for PayVista. I explain the questions I would ask the client before accepting the project and why clarifying requirements, dependencies, and responsibilities is essential for successful delivery. *Topics covered:* Business requirements: Balancing fraud detection and false positives Data availability and quality Model performance: What if the LLM does not outperform XGBoost? Infrastructure and GPU availability in AWS Mumbai Latency and response-time measurements Security, compliance, and data redaction API integration and documentation Testing, evaluation metrics, and acceptance criteria Deployment, rollback, and recovery approvals Project timelines and team availability Maintenance, ownership, and project handover The goal is to ensure that the client and development team agree on measurable outcomes, technical dependencies, timelines, acceptance criteria, and ownership before development begins. This presentation is part of my learning journey in AI and Generative AI, where I explore real-world project scenarios and practical software engineering concepts. If you find this video useful, please *Like, Share, and Subscribe* for more content on AI, Generative AI, software development, and real-world project discussions. #ProjectSentinel #ArtificialIntelligence #GenerativeAI #FraudDetection #SoftwareEngineering #ClientRequirements #AIProjects #EuronSuper30