The complete AI-visibility index for commercial debt collection agencies.

What AI already says about this category

Ninety seconds. AI answers this category from two places at once, what the models already remember about it and what they retrieve live, and this index measures both. Every figure below is drawn from a panel further down this page, and none of it is an estimate.

The Delta Quadrant

Every agency placed by what AI remembers (model memory) against what AI finds when it searches (live retrieval). Bubble size is mention volume. Tap any agency to focus it; switch Layer, Platform or Signal to move the category.

Blended leaderboard

Memory and retrieval for every entity; blended is the weighted headline (w_p 0.32 / w_g 0.68). Tap a row to focus it across the whole index. The chip shows recommendation quality: how AI frames the agency when it names it, a top pick (green) versus hedged with caveats (red). Hover for the recommended / neutral / cautioned split.

How this index is scored

Two layers, scored on the same five signals. The layers are what AI knows about an agency without looking versus what it finds when it searches. The signals are how each layer is measured.

Head-to-head, honestly measured

When AI compares two agencies, the naive answer is mostly an artifact of which is named first. We ask every matchup both ways; the number to trust is order-balanced. Toggle to watch the correction.

Attribute association

Which factors the models associate with each agency. Darker cell = stronger association. Each row is a share of that agency's own mentions, so an agency needs at least 20 mentions before its shares mean anything.

Search fan-out

One question quietly becomes many. These are the hidden sub-searches the models run to answer a category question, the content brief they wrote themselves: the debtor's industry and process dominate.

AI citations

Citation rate and the domains AI leans on.

Citation flow

Read left to right: the kind of question on the left, the source AI cited on the right. Aggregators and a broad long tail dominate; agency sites are a thin slice.

Citation sources & page types

Of the agency pages AI does cite, the mix concentrates in vertical page (26%) and homepage (23%). The chart below sets that against where citations go overall, across 6,405 cited URLs.

Google AI Overviews

An AI Overview appears on 63% of the 120 category keywords, the highest-volume AI surface. Even Google's own AI leans on third parties over the agencies: agencies are named mostly on their own brand terms, and on non-branded discovery queries the naming concentrates on a few. The count below is how many keywords return an AI Overview naming that agency, out of 120. It is not a count of citation instances: one Overview naming an agency twice still counts once. Tap to focus.

Organic share of voice

Share of estimated addressable organic clicks. The 16 highest shares of 39 measured entities are shown; the totals below the table cover all 39.

SERP and keyword coverage

Keyword-universe SERP appearances per agency.

"not ranked" means the agency ranks for none of the tracked keywords, so no composite is computed. That is absence from this keyword universe, not a score of zero.

Category concentration and saturation

How many cohort SERP appearances each question category actually has, and how concentrated they are where there are enough to tell. A concentration index over a handful of appearances reports the reciprocal of that handful, not the shape of the category, so it is withheld under 30.

Read the appearance counts first. 288 of 403 cohort SERP appearances land in the residual bucket rather than any named question category, so the keyword universe barely reaches the categories the AI prompt set asks about. That gap is the finding here; a concentration index computed over the handful that do land in a named category would only report how few there are.

White space

High-volume query clusters where no cohort agency has built a strong position.

AEO vs SEO

How AI visibility diverges from organic standing. AI rank here is the blended composite, because this vertical has no second collection occasion and so no measured band on any single dimension.

Entity explorer

Per-agency two-layer and organic detail. Tap any row to open the five signals behind its memory and retrieval scores.

Freshness of cited pages

A spot check, not a ranking: only 2 tracked agencies own a page in this category's most-cited set at all, and the median age of those pages is withheld for the reason stated under the table. What the panel does show is whether the page exposes a machine-readable date, which is what AI reads currency from.

Median age is withheld where the cited page reports a last-modified date later than this run's collection date. Those pages are current, but their age cannot be measured against a collection date that precedes them, so no number is published for them.

Citability infrastructure

Whether each agency has the machine-readable infrastructure AI leans on to recognize and cite an entity: structured data, an encyclopedia presence, an AI-crawler guide, and named authors.

Withheld columns: Wikidata (identical for all 39 entities, so it carries no information). A failed or rate-limited lookup is recorded as an absence, so those checks would report that named organizations have no entry. The score is computed over the 4 check(s) that remain.

Category findings

7 findings this index establishes about the category, each one carrying the measurement from the panel that produced it. These describe the category, not a single agency: the page-level, prioritised plan for one organization is a GEO Audit, which is a separate engagement.

These are findings about the category, measured on the whole tracked cohort. This index does not tell any one agency what to change: the page-level plan for a single organization (the specific URLs, entities, schema and crawler fixes, sequenced with owners) is a GEO Audit, a separate engagement.