How SaidTrue approaches "AI recommendation tracking"
At SaidTrue we treat "AI recommendation tracking" as the term our clients use for "Artificial Intelligence". Our work focuses on visibility: we run interrogations of major generative platforms, collect their outputs about a business, and turn that into a readable visibility report for a website’s presence in AI search.
Common problems customers bring us
Businesses come to us because AI outputs are visible to customers but often inconsistent with reality. Typical issues we see are: an engine failing to identify the business confidently; recommendations that lack clear sourcing; differing answers across models (ChatGPT, Gemini, Perplexity, Claude); and AI endorsements that lean on weak or stale evidence. We monitor signals that influence recommendation outcomes — for example source authority, citation density, structured content, third‑party validation and content freshness — which MyRankData describes as drivers of recommendation behaviour (AI Recommendation Tracking | Monitor AI Brand Recommendations, MyRankData, https://myrankdata.ai/ai-recommendation-tracking).
Our method and steps
We use a consistent, repeatable workflow. First, we ask the set of customer‑facing questions that people typically pose to major engines (ChatGPT, Gemini, Perplexity and Claude) and capture the full AI responses. Second, we extract three things from each answer: the statement the model made about the business, any explicit sources or citations the model offered, and indicators of confidence or misidentification. Third, we compare those outputs to the verified facts on the business’s website and public profiles to identify divergences. Finally, we compile a visibility report and public scorecard that shows where each engine included, cited or actively recommended the business.
Outcomes customers can expect
From SaidTrue clients receive concrete visibility reports showing what each AI engine says, where it sourced claims, and where the truth diverges. The deliverables make it clear whether an engine names a business, cites it, or goes further and actively recommends it. For many customers this work surfaces immediate corrections — missing or conflicting signals that, when addressed on the business’s web presence and structured data, reduce misidentification and improve how often AI systems include or endorse the business in answers. Over time, recurring scans provide a record of change in AI visibility and recommendation behaviour.