Retrieval visibility
Can priority pages be found and retrieved for the target question set?
Evidence: Crawl records, indexed URLs, repeated query capturesMethodology
The Nexus AI Visibility Framework.
A documented path from access to understanding, corroboration, observed visibility and business attribution. Each layer has its own evidence and limitations.
Authority corroboration
Do independent sources support the brand, service and claims?
Evidence: Source register, consistency review, citation evidenceEntity clarity
Are organization, people, services and markets unambiguous?
Evidence: Visible facts, schema parity, entity mapContent extractability
Can a system isolate useful answers, proof and limitations?
Evidence: Semantic HTML, answer blocks, headings and tablesTechnical access
Do status codes, robots, canonical and rendering support access?
Evidence: HTTP checks, robots, sitemap, no-JS reviewMeasurement & conversion
Can visibility be connected to referrals and qualified leads?
Evidence: First-party events, referrers, UTMs, CRM evidenceTest protocol
Repeatability before a score.
- Approve market, language, audience and commercial query themes.
- Fix the query set and competitor universe before collection.
- Record platform, date, account/market context and answer variability.
- Separate brand mention, cited mention, source and human referral.
- Attach evidence items and document alternative explanations.
- Repeat on the agreed cadence; change the set only through a changelog.
Guardrails
What the framework refuses to do.
No guaranteed placements
Answer systems are variable and controlled by third parties.
No crawler inflation
Bot fetches never become users, sessions or conversions.
No hidden claims
Schema and llms files repeat visible, approved information.
No score without formula
Component evidence remains inspectable even when a summary is used.
Start with evidence
Build a baseline your team can reproduce.
Start with one market, one commercial cluster and a fixed evidence protocol.