Measure whether your site can be found, understood, and used as a source.
Audit the technical foundations behind search visibility and AI-answer retrieval without hiding the evidence behind one unexplained score.
Check whether search and documented AI crawlers can reach the intended content.
Review canonical HTML, answer structure, entities, citations, and metadata.
Keep score inputs, sampled observations, and changes available for review.
Index Instrument measures AI visibility as a set of observable signals rather than a promise that a model will cite a page. It checks crawler access, indexable pages, source structure, entity clarity, machine-readable files, and sampled visibility evidence. Each score links back to the conditions that produced it.
AI visibility is not one universal rank
Search engines and answer products use different crawlers, indexes, retrieval systems, and presentation rules. A single percentage cannot describe all of them unless the platform explains what was sampled and how the number was calculated.
Index Instrument separates technical readiness from sampled visibility. Technical checks can show whether a crawler is allowed, whether the canonical page is retrievable, and whether the content provides a usable answer. A sampled model response can show what happened for one recorded question, engine, locale, and date.
Fix the source before chasing prompt fluctuations
A model answer may change between runs. Broken canonicals, blocked resources, thin pages, or contradictory product facts are more stable problems. Repairing those issues improves the source even when a specific answer engine does not cite it every time.
The platform groups repeated technical findings so teams can fix the shared template or configuration first. That is usually more useful than treating every affected URL as a separate project.
- Crawler access and robots directives.
- Canonical, sitemap, and hreflang consistency.
- Answer-first structure and attributable claims.
- llms.txt and machine-readable content consistency.
- Repeat checks after deployment.
Keep reporting honest
The report distinguishes observed HTTP evidence, third-party documentation, sampled model behavior, and model-generated interpretation. These are different kinds of evidence and should not be merged into one confident claim.
For business measurement, combine the audit with Search Console, analytics, and CRM outcomes. Visibility matters when qualified visitors, demos, or sales conversations follow.
One process with visible approval points.
Establish the baseline
Run a public scan and record the current technical readiness scores.
Inspect the evidence
Open the conditions behind access, retrieval, structure, and discovery findings.
Repair shared causes
Prioritize templates and site-wide rules before isolated page edits.
Measure the change
Repeat the audit and compare the same evidence before and after deployment.
What customers control.
Can an AI visibility platform guarantee citations?
No. It can improve and measure the conditions that make a page usable as a source, but retrieval and citation decisions remain specific to each product and query.
What is included in an AI visibility score?
Index Instrument exposes the contributing checks instead of treating the score as a black box. These include access, indexability, source structure, discovery files, and other documented readiness signals.
How should a company measure business impact?
Track audit changes alongside Search Console queries, organic landing pages, referrals, qualified conversions, and CRM outcomes. Sampled citations should support that analysis, not replace it.
Check the site before deciding how much work it needs.
Quick Scan gives you a free baseline. A paid audit opens the complete findings and remediation plan.
Run a free Quick Scan