Mengwei Local Leads Workbench

Half-developed local leads workbench prototype for AI content, video automation, keyword research, account warming, and publishing workflow study.

Digital Marketing & SEOPythonMIT

Abstract

Mengwei Local Leads Workbench is an open-source Digital Marketing & SEO project. Half-developed local leads workbench prototype for AI content, video automation, keyword research, account warming, and publishing workflow study. It is built using Python, FastAPI. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Digital Marketing & SEO mini project or final-year project.

1. Introduction

Half-developed local leads workbench prototype for AI content, video automation, keyword research, account warming, and publishing workflow study.

2. Objective

Half-developed local leads workbench prototype for AI content, video automation, keyword research, account warming, and publishing workflow study.

This project demonstrates how Python, FastAPI can be applied to a real-world Digital Marketing & SEO problem.

4. Technology Stack

PythonFastAPI

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/22kk12/mengwei-local-leads-workbench.git
cd mengwei-local-leads-workbench

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Export reports to Google Sheets or PDF
  • Schedule weekly automated reports
  • Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. Which marketing or SEO problem does this tool solve?
  2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
  3. Which metrics or KPIs does it report and how are they calculated?
  4. How could the output help a business make decisions?
  5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by 22kk12 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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