Brand visibility in AI answers is volatile. The same prompt can name you in ChatGPT, ignore you in Claude, and surface a competitor in Perplexity — and those results drift week to week. Tracking it manually doesn't scale. Here's a framework that does.
1. Build a prompt set that mirrors real buyers
Start from the questions a prospective customer would actually ask: "best CRM for startups," "top influencer marketing platforms," "alternatives to [competitor]." These category and comparison prompts are where recommendations get made.
2. Run the same prompts across every engine
Coverage matters. ChatGPT, Gemini, Claude, and Perplexity each weight sources differently, so a brand can be strong in one and absent in another. Track them side by side to see the full picture rather than a single engine's view.
3. Score mentions, not just presence
- Is your brand named at all?
- Is it named first, or buried after competitors?
- Is the description accurate, or outdated?
- Which sources is the engine citing to justify the answer?
4. Watch the trend, not the snapshot
A single scan tells you where you stand today. Daily scans tell you whether you're gaining or losing ground — and whether the content and citation work you're doing is actually moving the needle.
Oras automates this end to end: daily scans across every engine, yesterday-vs-today mention tracking, competitor comparisons, and a single GEO score so you can see progress at a glance.