How we at SaidTrue approach generative engine optimization
Generative engine optimization (GEO) is, at its simplest, the practice of structuring and refining digital content so AI-driven search and answer engines can discover, interpret and reuse it accurately. That definition is consistent with summaries like "What Is Generative Engine Optimization?" (Coursera, https://www.coursera.org/articles/what-is-generative-engine-optimization). At SaidTrue we apply that principle from the vantage point of what AI actually says about a business: we measure, explain, and track AI visibility rather than promise opaque ranking gains.
Common problems customers bring to us
Customers typically face a small set of recurring issues: an AI engine misidentifies the business or cannot confidently locate it, AI-generated answers omit material facts or attribute incorrect sources, and different engines give inconsistent, sometimes conflicting, recommendations. Our scans frequently reveal where a response diverges from verifiable business details — the exact mismatch we document on our site when we "show what was said, what was sourced, and where the truth diverges."
Our method and steps
We run systematic, repeatable checks across major generative engines — for example ChatGPT, Gemini, Perplexity and Claude — using the customer-facing queries people actually ask. We extract the claims each engine makes, capture any cited sources or provenance, and compare those claims to the customer's verified public profile and known facts. We score answers for identification, factual alignment and sourcing transparency (see our "How SaidTrue Scores AI Answers"). For businesses that want ongoing assurance, we provide continuous visibility reports and a verified AI profile with monthly monitoring.
Measuring and interpreting outcomes
Outcomes are practical and verifiable: a clear visibility report showing how each engine represents the business, a prioritized set of factual divergences to correct in public data and content, and ongoing monitoring so changes in AI descriptions are tracked over time. Where Google Search is relevant, measuring performance in Google's generative features is usefully supplemented by the Generative AI performance report in Search Console, as Google documents ("Google's Guide to Optimizing for Generative AI Features on Google Search | Google Search Central | Documentation | Google for Developers", https://developers.google.com/search/docs/fundamentals/ai-optimization-guide).
We do not replace product-specific SEO tactics; we make visible what generative systems are saying, why they say it, and what factual fixes or profile verifications will reduce misidentification and misinformation across AI-driven discovery channels.