Methodology
How OpenCrawl measures AI visibility
OpenCrawl turns crawl signals and answer evidence into a transparent five-pillar score. Every weight below matches the product scoring engine.
Design principles
- Evidence over guesses. Scores come from fetch results and prompt outcomes, not vibes.
- Neutral prompts. Buyer-intent queries avoid branded stuffing so mention rates stay meaningful.
- Separate crawl from answers. Being crawlable is necessary but not sufficient for citations.
- No outcome guarantees. Measurement helps you improve; it does not promise rankings.
Five pillars and weights
| Pillar | Weight | What it measures |
|---|---|---|
| Crawl access | 25% | GPTBot / OAI-SearchBot allow signals and GPTBot homepage fetch success |
| Content readiness | 20% | Readable HTML, title, meta description, Open Graph, JSON-LD, headings, llms.txt, sitemap |
| ChatGPT search | 30% | Mention and recommendation rates across 8 live ChatGPT web-search prompts |
| Open-web coverage | 20% | Mention and recommendation rates across 12 Exa semantic searches |
| Training presence | 5% | Whether the domain appears in Common Crawl snapshots when checked |
Prompt routing
A full scan uses 20 buyer-intent prompts. Eight are evaluated with ChatGPT web search; twelve are evaluated with Exa. Recommendation rate is weighted more heavily than raw mention rate inside each answer pillar.
Grades
Composite scores map to letter grades: A (90+), B (80–89), C (70–79), D (60–69), F+ (50–59), and F (below 50). Score deltas compare against the previous completed scan for the same project when available.
FAQ
Why weight ChatGPT search highest?
Live ChatGPT web-search answers are a direct signal of how often your brand appears in AI-mediated buyer journeys, so that pillar is weighted at 30% of the composite score.
Are prompts written to favor my brand?
No. OpenCrawl uses neutral buyer-intent prompts so results reflect organic mention and recommendation rates rather than branded query stuffing.