aeo-entity-extractor

To extract entities from Gemini responses, you can run the extraction script across multiple Gemini outputs to identify recurring brands, statistics, and people. This reveals the topic vocabulary needed for consistent content planning and optimization.

Entity gap analysis compares high-frequency entities cited in Gemini responses against your domain's content to surface missing topics. It identifies vocabulary gaps to address for building stronger topic authority.

Yes, you need a Gemini API key to analyze recurring cited entities, along with Python 3.9 or higher. The tool queries Gemini directly to gather response data for extracting per-type entity rankings and top source domains.

Yes, you can configure the number of runs, model selection, and concurrency when extracting entities. This controls the volume of Gemini responses analyzed and optimizes processing speed for competitive benchmarking tasks.

You can map several types of entities from Gemini's topic vocabulary, specifically brands, statistics, people, tools, and URLs. The extraction produces human-readable summaries and machine-readable rankings to inform content strategy.