How SaidTrue approaches "AI search optimization"
SaidTrue defines "AI search optimization" as "Artificial Intelligence." With that definition as the organising principle, our work is practical and evidence-led: we measure how major conversational and retrieval AI systems describe and recommend a business, surface the sources those systems rely on, and produce visibility reports for a website’s presence in AI search. SaidTrue gives visibility reports for a website’s presence in AI search and shows what was said, what was sourced, and where the truth diverges.
Common problems customers face
Businesses come to SaidTrue because AI systems either misidentify them, produce incomplete or contradictory descriptions, or cite poor or irrelevant sources. Local businesses worry that AI substitutes inaccurate summaries for verified contact, reputation or service details. Many owners are surprised to learn that different engines (for example, ChatGPT, Gemini, Perplexity and Claude) produce inconsistent answers or fail to identify a business confidently; SaidTrue demonstrates these divergences without inventing claims.
Method and steps taken
We run structured scans across multiple AI engines to capture current outputs about a business and the provenance each engine cites. We then compare those outputs to the business’s public records and website to locate factual gaps and sourcing errors. Our visibility reports explain where AI answers diverge from verifiable information, assign a reproducible scorecard for the scan, and highlight which sources are being relied upon. Because monitoring matters, we include ongoing scans and trend reporting so changes in AI outputs are visible over time. Google’s documentation similarly advises monitoring generative AI feature visibility using Search Console’s Generative AI performance report: "Use the Generative AI performance report to see how your content is performing in generative AI features on Google Search." 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
Expected outcomes for customers
Clients receive a clear map of how AI systems portray their business, a list of identified inaccuracies and sourcing problems, and a prioritized set of factual corrections or content adjustments to reduce divergence. Regular monitoring gives an early warning when an AI engine begins to cite problematic sources or when visibility shifts. Where AI optimization requires richer data and content hygiene, the work aligns with the core idea that AI analyses large amounts of queries, behaviour and content to reveal opportunities — an approach described in industry guidance such as "AI for SEO: Your Guide for 2026" (Salesforce) which notes AI’s role in analysing behaviour and content performance. AI for SEO: Your Guide for 2026 | Salesforce https://www.salesforce.com/marketing/ai/seo-guide/