Building a Local AI Research Agent for Automated Wikipedia-Style Articles

Recently, I completed an interesting AI project for a client.

The goal was to build a system that automatically generates Wikipedia-style articles while ensuring that each article includes links to a specific website. This was a key requirement because the client’s business heavily relies on SEO.

As the foundation, I used Stanford STORM, an open-source AI research agent designed to generate high-quality, research-oriented articles. It automatically explores the web, gathers information from multiple sources, builds a well-structured outline, and produces comprehensive articles in an encyclopedic style.

One of the project’s main goals was to keep operating costs as low as possible, so the entire system runs on a local LLM instead of expensive cloud-based models.

I customized STORM to fit the client’s workflow by:

• Integrating support for a local language model.
• Modifying the research and content generation pipeline.
• Adding logic to always use information from the client’s website as one of the research sources.
• Ensuring that relevant references to that website are automatically included in every generated article.

The result is a fully automated AI system that creates high-quality research articles while incorporating the client’s resources into the reference list. This supports the client’s SEO strategy and removes a significant amount of manual work.

Projects like this remind me that the real value of AI isn’t just using large language models—it’s adapting them to solve real business problems efficiently.