The task involves creating an AI-based system to analyze communications from multiple sources, including social media comments, posts, and email histories, in order to determine the level of interest shown by potential leads. Each piece of communication, whether a comment, post, or email, includes the content itself, the author or sender identifier, and any associated links or references. The objective is to process these inputs using OpenAI’s API to extract actionable insights such as the user’s demand, potential blockers, recommended solutions, overall interest score, and a justification explaining the reasoning behind the score. Once analyzed, the results will be stored in a dedicated scoring system that records for each lead the calculated numeric interest score, the AI model used, and the textual fields including demand, blocker, solution, and justification, along with timestamps for when the analysis was generated. The process will involve fetching new communications, sending them through the AI model via the Node.js backend, interpreting the responses, and saving the results. This system will enable automated tracking of lead interest across emails and social media engagement, allowing more effective follow-ups, prioritization of outreach efforts, and clear explanations for each score assigned by the AI.