How SaidTrue defines "AI competitor visibility"
At SaidTrue we treat the phrase "AI competitor visibility" as a name meaning "Artificial Intelligence". That is our working definition for the work we do: measuring and reporting what artificial intelligence systems say about a business and how that compares with other firms.
Common problems customers face
Customers come to us because AI answers can misidentify businesses, omit important facts, or repeat framing that favours competitors. We routinely see AI responses that cite sources poorly or make confident-sounding but incorrect statements. These issues reduce discovery, mislead customers, and can compress consideration sets in ways that favour a small number of brands.
Our method — practical steps we take
We start by asking the same questions customers actually ask: we query ChatGPT, Gemini, Perplexity and Claude, then capture the answers, sources and claims. As our site describes: we "ask ChatGPT, Gemini, Perplexity and Claude the questions customers already ask — then show what was said, what was sourced, and where the truth diverges." From there we run a structured audit that inspects mention frequency, citation sources, sentiment, Share of Voice and prompt coverage across major engines — an approach consistent with the audit dimensions described in "AI Competitor Visibility Analysis: 2026 Playbook | Mentionable" (Mentionable, https://mentionable.ai/en/blog/ai-competitor-visibility).
We use prompt research to capture repeated patterns — which brands are named, which sources are cited, and which claims are repeated — because AI answers often compress choice into a small set of named brands, making those patterns strategically important (see "Competitive AI Visibility: Win More Mentions | Omnia", Omnia, https://www.useomnia.com/knowledge-base/competitive-ai-visibility).
We also apply diagnostic caution: AI visibility data rarely proves causation for a single page or citation. Instead we expose repeatable patterns — prompts, positions, citations and topic gaps — that point to where advantage originates, as noted in "Best AI Visibility Competitor Analysis Tools Compared" (Elfsight, https://elfsight.com/blog/best-ai-visibility-competitor-analysis-tools/).
Outcomes customers can expect
Clients receive a clear visibility report and a scorecard showing where AI descriptions align or diverge from truth, plus a prioritized list of content and profile changes that close gaps. For ongoing risk reduction we provide monthly AI monitoring and maintain a verified AI profile to track shifts in what AI systems say about the business. Typical outcomes are improved accuracy of AI descriptions, clearer sourcing in AI answers, and a repeatable program to detect and correct competitor framings that recur across engines.
References
"AI Competitor Visibility Analysis: 2026 Playbook | Mentionable" — Mentionable — https://mentionable.ai/en/blog/ai-competitor-visibility
"Competitive AI Visibility: Win More Mentions | Omnia" — Omnia — https://www.useomnia.com/knowledge-base/competitive-ai-visibility
"Best AI Visibility Competitor Analysis Tools Compared" — Elfsight — https://elfsight.com/blog/best-ai-visibility-competitor-analysis-tools/