RadiusRankServices

Evidence-led AI search service

AI Visibility Recovery

AI visibility recovery begins by proving that a decline is comparable. RadiusRank checks whether the question set, provider, dates and sample changed before diagnosing technical access, stale evidence, entity confusion or stronger competitor sources.

Updated 6 minute service brief

Scope and boundaryThe service can diagnose and prioritize recovery work. It cannot restore a previous answer on demand or guarantee that a provider will select the brand again.
5 causesrecovery hypotheses tested
2026-08-11SE Ranking demand snapshot
0ranking or citation guarantees

What the evidence supports

  1. No reliable exact-match demand row was present in the saved competitor export, so this page uses a falsifiable recovery method instead of a claimed search volume.
  2. A changed prompt or provider can create apparent decline without any site change.
  3. Technical failure, evidence decay and competitive displacement require different recovery actions.

Deliverables and acceptance evidence

A deliverable is not complete because a document exists. Each phase has observable acceptance evidence and a boundary on what it can prove.

WorkstreamShipped outputAcceptance evidence
ComparabilityQuestion, provider, model label, market and sample auditConfirm like-for-like decline before diagnosis
Site evidenceFetch, render, canonical, content and entity change reviewDated issue evidence tied to affected pages
Answer ecosystemChanged competitors and cited sourcesSource-level explanation for displacement hypotheses

What the engagement should change

01

Rule out sample drift

Reconstruct the previous and current denominator before changing the site.

02

Repair the failed layer

Fix access, clarity, proof or corroboration according to observed evidence.

03

Re-run comparably

Use the same question and provider conditions and report the remaining uncertainty.

Methodology

  1. Use a fixed buyer-question and query map so content, technical work and measurement share the same decision scope.
  2. Capture the baseline before implementation, including exact sample sizes, engines, dates, rankings or answers used.
  3. Ship the smallest evidence-backed technical, content or authority change that closes the gap, then record what actually went live.
  4. Re-run comparable observations and report counts alongside percentages. Do not infer revenue without authorized commercial data.

Limitations

  • Historical consumer answers may not be reproducible.
  • Provider-side changes can affect visibility without a public explanation.
  • Recovery work cannot guarantee restoration of a probabilistic answer.

Questions

Can ai visibility recovery guarantee an AI citation?

No. The work can improve accessibility, clarity, evidence and external corroboration, but the answer system decides what it retrieves, names and cites.

How is progress measured without customer Search Console access?

RadiusRank can use a stable question sample, raw answers, mentions, citations, source share and public technical checks. Search and revenue claims are added only when the customer authorizes those data sources.

Sources and data

Found a changed price, product limit or source? Email team@radiusrank.com with the official URL. We preserve the snapshot date instead of silently rewriting historical data.

Start with the evidence

Start with an inspectable baseline.

The free diagnostic shows the questions, public technical issues and citation-readiness gaps before you start a subscription or scoped service.

Run the free diagnostic