Built for Decision Moments
When the decision is whether to pay for a specific placement — or how hard to push for better positioning — the PQS gives you data to back it up.
PR & Comms Teams
Justify the spend on a specific placement by quantifying its AI citation potential. Negotiate better positions with data showing the value difference between the #1 slot and a passing mention.
Marketing Agencies
Audit your clients' existing backlink portfolios to find which placements are delivering AI citation value and which are wasted. Bring data to the renewal conversation.
Content & SEO Teams
Use chunk containment and document-position guidance to craft self-contained mentions that AI systems actually cite. Optimize the placements you do control.
Five Signals, One Score
The PQS combines five research-backed factors that determine whether a specific brand mention on a specific page is likely to be surfaced by AI assistants.
Document Position
Large language models disproportionately cite content from early in a document — research shows 44% of citations come from the first 30% of content. We find where the brand mention sits and score it against the published citation-probability curve.
Chunk Containment & Sentiment
AI models process content in fixed 128-token windows. We evaluate the chunk containing the brand mention plus its neighbors — is it self-contained? Is the sentiment around the mention balanced, strongly positive, or critical? Sentiment can amplify or cancel the score.
Placement Type, Editorial Control & Link Attribute
A dedicated article scores differently than a passing mention or a buried sidebar. A dofollow link signals differently than a nofollow or sponsored link. We detect these automatically from retrospective placements or let you enter them for hypothetical ones.
Two Modes, Real Signals
Whether the placement exists or you're evaluating a proposal, the PQS gives you defensible numbers.
Retrospective Scoring
Paste a live URL and we fetch the page, tokenize it, locate the brand mention (including aliases), detect the link's rel attribute, and classify the placement type and sentiment with an AI model.
Prospective Scoring
Evaluating a placement opportunity before it exists? Enter the publisher, placement type, position, and link attribute to get an instant score. Preview the lift from negotiating for better terms.
Combined with AIPVS
When the publisher is known, we combine the page-level PQS with the domain-level AI Placement Value Score to give you a Total Placement Value — the score that answers both 'is this domain worth it?' and 'is this placement well-positioned?'
Key Message Pull-Through
For retrospective placements, we use semantic similarity to check whether your property's key messages actually come through in the content — shown alongside the score as informational context, not as a scoring factor.