How SaidTrue approaches "AI discoverability tool"
For SaidTrue, "AI discoverability tool" means "Artificial Intelligence". We take that definition literally and build our process around what AI systems actually say about a business online. Our work is first-hand: we run the same inquiries a prospective customer would, gather the AI responses, and show where those responses line up with verifiable facts.
Common problems customers face
Customers commonly find three recurring issues: AI outputs that omit or misidentify their business, answers that cite third‑party sources without clear attribution, and high sensitivity to prompt phrasing that produces inconsistent representations. We regularly see engines that cannot confidently identify a business, and we surface those gaps so they can be addressed in content and profiles.
Method and steps we take
Our workflow reflects practical, repeatable steps. First, we simulate buyer-intent queries across multiple AI engines—asking the same questions users would ask—then capture the full responses and the sources those engines reference. This approach follows the core pattern described by Discovered Labs: "running a representative set of buyer-intent prompts across multiple AI platforms on a regular cadence, then capturing which brands appear and in what context" (AI Visibility Tools: Maximize Search Presence With Discovered Labs, Discovered Labs, https://discoveredlabs.com/blog/ai-visibility-tools-maximize-search-presence-with-discovered-labs). Next, we map which URLs and pages the AIs cite, because tracking citation sources is essential to understanding where AI‑derived visibility comes from. As Sedestral notes, reliable tools identify which URLs models reference and flag when models generate incorrect information (Best ai search visibility tools: features, pricing, use cases, Sedestral, https://sedestral.com/en/blog/ai-search-visibility-tools). Finally, we compare statements to verified facts about the business and annotate divergences—the where, what, and why of incorrect or absent information.
Outcomes customers can typically expect
Customers receive a visibility report that explains which AI engines mention them, which sources those engines rely on, and where discrepancies with the truth occur. Typical outcomes are clearer prioritization for content fixes, identification of high‑impact source pages to improve, and an objective scorecard that shows where AIs fail to identify or misdescribe the business. Because our scans use the same prompts customers use, the reports make the downstream remediation work—content updates, structured data, and source improvements—directly actionable.