AI-Powered Business Automation Project مشروع أتمتة تجارية باستخدام الذكاء الاصطناعي Company / الشركة Food Company - Chocolate & Sweets / شركة حلويات وشوكولاتة Industry / القطاع Food & Beverage / الأغذية والمشروبات Technology / التقنية n8n + Google Gemini AI + Telegram

Weekly Sales Report Automation - Arabic Gulf Market The Problem: Sales managers in Arabic Gulf businesses spend 2 to 4 hours every week writing sales reports manually, which leads to errors and inconsistent results. My Solution: I built an automated n8n workflow that reads live sales data from Google Sheets, analyzes it using Groq AI, and sends a professional Arabic report to Gmail automatically in under 2 seconds, with no manual effort needed. Workflow Integrations: Google Sheets reads the weekly sales data. Groq AI (LLaMA model) generates the Arabic narrative report. Gmail delivers the report to the recipient automatically. Real Data Testing: The workflow was tested on real chocolate and sweets sales data from the Arabic Gulf market. It processed 8 rows and delivered the report successfully in 1.142 seconds. Business Impact: This automation saves 2 to 4 hours of manual work every week, eliminates calculation errors, produces consistent professional Arabic reports, and scales to any data size without extra effort.

Project Title: Predictive Maintenance of Turbofan Engines Using NASA C-MAPSS Dataset Overview: This project applies machine learning to predict the Remaining Useful Life (RUL) of aircraft turbofan engines using NASA's C-MAPSS FD001 dataset, which contains 21 sensor readings across 100 engines. Key Steps: Cleaned and preprocessed 20,631 rows of sensor data Removed 7 non-informative constant sensors, reducing features by 33% Identified 3 degradation patterns across retained sensors Built a Linear Regression baseline model Results: Test RMSE = 31.95 cycles ✅ (matches published benchmark of ~31) R² Score = 0.41 on test set AI Tools Used: Claude AI (Anthropic) was used for pattern analysis, feature interpretation, and documentation.

This analysis was performed using Claude AI, where the dataset was processed to identify trends, correlations, and key performance insights. The tool helped generate data-driven conclusions efficiently and accurately

Initial Issues 1.Some AI responses lacked accuracy 2.Simple and non-professional UI 3.No multi-language support 4.No file upload feature 5.Limited user interaction Improvements 1.Improved AI response accuracy and structure 2.Added file upload (PDF & text) with automatic summarization 3.Enabled English & Arabic support with RTL 4.Enhanced UI to be more modern and organized 5.Added contact options (WhatsApp & Email) App link: https://lesson-wiz-ai.lovable.app/