The engines already told you where they look.
Every stored answer names the pages the engine trusted. RankSage reads back across all of them and surfaces the external pages engines cite again and again in your space — with your brand nowhere on them. That is not advice; it is a target list.
The problem
Most AI-visibility advice ends at “get cited more”, which is not a plan. The specific pages worth being on are sitting in the answers you have already collected — nobody reads them back out.
Placement Targets on one screen.
What it measures, and what comes out.
Outcome and evidence at each step, never the recipe. Every number here can be traced to something stored.
- 01
Built from answers you already have
This layer runs no new queries. It reads across every stored answer — the citations, the domains, the entities — and extracts what the engines have already revealed about where they look.
- 02
Placement targets, ranked
External pages that engines cite repeatedly in answers about your space, where your brand does not appear. Each carries which engines cite it and how often — the case for a guest contribution, a listing, or a mention, made with the engine's own behaviour.
- 03
The company you should keep
The entities engines repeatedly associate with your topics — categories, standards, adjacent tools. Being described alongside them is how an engine learns what you are, and the list shows which associations you currently lack.
- 04
Every offsite mention, mapped
A read-only map of every third-party URL engines have cited across your runs: the domain, which engines cite it, when it was first and last seen, and whether your brand is known to appear on it from the answers already held.
- 05
Extraction, never generation
Only what appears in stored answers can appear here. The extraction runs against a closed schema, so there is no step where a model could invent a URL, a mention, or an association that was never observed.
What it does not do.
Disqualifying fast is part of the product. These are the edges, written down.
The lens is your tracked prompts. Pages cited only in questions you never ask will not appear.
It names where to show up. The outreach, the listing, the contribution — that work is still yours.
The mention map reflects what stored answers revealed. Pages are not re-fetched to check whether they have changed since.
The capabilities it joins.
Read the longer version.
Questions about Placement Targets, answered.
What it measures, what it needs, and where its edges are.
Partly by improving your own pages — and partly by being present on the pages engines already trust. Placement Targets names those pages from your stored runs: external URLs cited repeatedly in your space where your brand is absent, ranked by how often engines lean on them.
From the answers RankSage has already collected for your tracked prompts. Every citation in every stored answer is read back out and aggregated — no new queries, no model guessing which sites might matter.
No. It gives you the evidence — which pages, which engines, how often, and whether you are mentioned — and the work of earning the placement stays with you. A tool that promised the rest would be overselling.
AI is already answering for you.
Sign in to Google Analytics and Search Console. Read-only, about five minutes, no code on your site for that step.
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