I have a large file of Gmail addresses and I want a Python-based desktop application that can tell me two things for each address: the company the user works for and the current CEO of that company. To make this possible, the app should blend Natural Language Processing, Machine Learning and smart Web Scraping. I picture dropping in a .csv or .txt list, hitting “Start”, and watching a progress bar as the software crawls reliable sources, cross-checks the data and returns a clean spreadsheet (or JSON) with three columns: email, company, CEO. Core expectations • Built with Python so I can run it locally on Windows or macOS without extra setup. • Uses NLP to decipher any clues inside the email string itself, ML for pattern matching and disambiguation, and Web Scraping to pull publicly available info (LinkedIn, Crunchbase, official company sites, etc.). • Respectful scraping: follow robots.txt, rotate user-agents, back-off timing. Acceptance criteria 1. I feed 100 test Gmail addresses; at least 80 come back with a company name and CEO that can be manually verified. 2. No captchas or IP blocks during a 100-address run. 3. Source code, requirements.txt and brief setup guide included. If you’ve built contact intelligence or enrichment tools before, let me know. I’m ready to get started as soon as you can confirm approach and timeline.