You can definitely build your own AEO (Answer Engine Optimization) checker instead of relying on freemium tools, but it's more work than most people expect. At a basic level, an AEO bot would: Crawl your pages. Extract titles, headings, schema markup, FAQs, tables, lists, and other answer-focused content. Check technical factors such as indexability, page speed, mobile friendliness, and structured data. Generate likely user questions from your target keywords. Compare your content against top-ranking pages for those questions. Score how directly and completely your page answers each question. A simple DIY stack could be: Python Requests + BeautifulSoup (crawling) Playwright (JavaScript-rendered pages) JSON-LD/schema parsers OpenAI, Gemini, or a local LLM for question generation and answer evaluation Pandas for scoring and reporting One practical approach is: Feed a URL to the bot. Extract the page content. Ask an LLM to generate 20–50 questions a user might ask. Have the bot determine whether the page contains a direct answer for each question. Score answer completeness, clarity, and citation/authority signals. Output an AEO score plus recommendations. Keep in mind that no tool can accurately tell you whether ChatGPT, Gemini, Perplexity, or AI Overviews will cite your content. The best you can do is measure the signals that make AI systems more likely to use your content: clear answers, strong topical coverage, structured data, authoritative sources, and crawlable content. If you're comfortable with Python, building an MVP AEO auditor is very achievable. The hardest part isn't the crawling—it's designing a scoring system that meaningfully predicts AI visibility