I need a Python system that runs nonstop, scours the web for fashion, clothing, and footwear brands or sellers only for india, extracts every piece of publicly available business contact data it can find, pinpoints decision-makers when their names or roles appear, then verifies each email address with DNS-level checks only—no third-party validation services. All captured information must immediately land in clean, well-structured Excel workbooks, so I can open the file and start outreach without touching a database. Reliability is critical: the workflow has to restart itself after a crash, pick up where it left off, and scale out smoothly if I decide to add more crawling threads or containers later. Package everything inside a Linux-compatible Docker image; a single docker-compose up command should spin up the full pipeline. Deliverables • Full, readable Python source code organized into clear modules • Dockerfile and docker-compose.yml configured for Linux hosts • An example Excel file that proves the schema and shows sample scraped contacts • Setup & run instructions (markdown or plain text) • Brief note on how the system self-heals and the exact DNS checks performed (syntax, MX, Catch-All detection, etc.) Acceptance criteria 1. Continuous discovery and scraping run successfully for 24 hours in a test environment without manual intervention. 2. At least 95 % of exported email addresses pass the specified DNS validation steps. 3. A fresh environment can be provisioned with only Docker installed by following the supplied instructions, producing the same results.