Lighthouse++ - A Search and Generative Engine Optimization Tool for VSCode

Abstract

Search engine optimization (SEO) remains an important challenge for e-commerce businesses in the ever-expanding and competitive World Wide Web (WWW) landscape. The emerging AI-driven information retrieval trends have added new dimensions to the WWW utility and further complicated the SEO problem. Traditional SEO is largely performed using external tooling, disrupting the flow as developers move back and forth between environments. In addition, current tools do not adequately consider the evolving AI-driven search landscape. This thesis presents the design and implementation of Lighthouse++, a Visual Studio Code (VS Code) extension for automated analysis and improvement of webpage quality with a primary focus on on-page SEO and Generative Engine Optimization (GEO). The system is built around Google Lighthouse and extends it with AI-assisted suggestions, a set of custom metrics, and an exploratory GEO component intended to provide heuristic feedback rather than predictive ranking-based estimates. In this regard, the thesis proposes a GEO-oriented evaluation framework based on semantic similarity and source visibility, intended to capture how webpage content may align with retrieval and answer generation in large language model systems. The developed extension audits webpages, identifies technical and structural issues, and generates suggested code changes intended to improve Lighthouse-related outcomes while also exposing GEO related feedback as an exploratory complement to traditional SEO analysis. Finally, Lighthouse++ comes with improved usability by enabling developers to code and test their websites for S&GEO in their IDEs. The project demonstrates the effectiveness of the proposed tool by evaluating and showing improved metrics across 80 pages from 18 different websites. The evaluation shows an increase in all metrics and that AI-assisted SEO and GEO are possible.