Original Summary

Hey everyone! I need some tips from those who have built something similar. I'm building a tool where the user describes campaign results in text (like "I spent $3,000, got 34 sales at $450 each, and 210 people interested") and the AI returns a diagnosis of what's working, what's not, and what to do next. All in simple language, no acronyms like ROAS, CTR, or CAC. The architecture I'm thinking: - Frontend on Lovable - Backend on Supabase with RLS and multi-tenant - AI engine with three layers: Analytics AI (insights), Opportunity Engine (recommendations), and Rules Engine (automation) What I already have working: - Client area with New Analysis, Overview, and Reports - AI already generates insights based on the data entered - Multi-tenant configured What I'm still developing: - Rules Engine (IF/THEN) - Button to apply recommendations directly to the campaign - Feedback Loop for the AI to learn from hits and misses My questions: Has anyone used AI to translate technical metrics into client-friendly language? How did you do it? For the Rules Engine, do you recommend storing rules in the database or in code? Any tips for implementing an efficient Feedback Loop? Any help is welcome. Thanks!   submitted by   /u/DavviCostt [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/18 21:00:51