AI Discovery Intelligence
Most AI visibility tools tell you whether you show up, and stop there. We measure what ChatGPT, Claude, Gemini and Perplexity say about you, tie it to your traffic and cost, and do the work that moves it.
$9,000 for six months, one market. Marketplaces scoped separately. Written scope back within one business day.
First pick in 45 of 160 open answers, up from 35. The pattern: answers that cite a Northvale page name Northvale 81% of the time; answers that don’t, 20%. The #1 move: Meridian Home owns 9 of the 12 Discovery questions on delivery and returns pages Northvale doesn’t have - that gap is this month’s build.
| Query | Sep 1 · Latest | ||
|---|---|---|---|
| 1 | best sustainable furniture shops in the uk? | ||
| 2 | i'm furnishing a flat in london, where should i buy? | ||
| 3 | where to buy a solid oak dining table in the uk? | ||
| 4 | is handmade furniture worth the extra cost? |
The client portal. Illustrative data for Northvale, our sample brand.
Four capabilities on one locked basis. Every figure a count you can re-derive from the answer record - no composite score, no black box.
01Question Panel
Sixty buyer questions per market, built from your offer and your competitors, approved by you, then frozen.
02Answer Intelligence
Every answer from ChatGPT, Claude, Gemini and Perplexity read and scored: #1 Pick Rate, Top 3 Rate, Visibility Rate, and the exact pages behind each one.
03Execution Playbook
Around a dozen ranked moves per market, each tagged we deploy, we draft and you publish, or together.
04Movement & Demand Intelligence
Identical measurement every month against the locked baseline, plus your server logs: the buyers who actually arrived from an answer.
Four engines in. Four capabilities. Three team-ready outputs.
Locked basis. Re-derivable counts. Executed moves. First-party demand.
The full 60-question panel, a named analyst, and the work that moves the number. Or size the problem first with a $1,000 Snapshot, credited in full if you continue. Marketplaces are scoped separately.
Case study
“When people ask AI where to buy near them, Scoop went from 9% of answers to 32% in three months. On ChatGPT we now show up in more than half.”
Iryna Nestsiarovich CEO, Scoop Wholefoods UAE
Three months, identical locked panelOn ChatGPT: now over half of answers
Architecture
Between your first-party data feeds and your analytics. An outbound feed: no SDK, no dashboard, nothing in your rendering path.
We measure what AI says about you, then we do the work to improve it. Every month re-measures against the locked baseline.
Your existing infra
AI-channel signal · CDP · paid spend feeds
↓
28 Labs layer
AI Discovery Intelligence
Recommendation share · Citation graph · Demand read · Spend read
↓
Your existing surfaces
Web analytics · Exec dashboards · Budget instruments · Build targets
What it looks like operationally
Each team gets what it needs, in the tool it already uses.
URL-level page specs, schema and linking patterns, mapped to where citation share is most winnable. We deploy where you give us access; otherwise your team ships from the spec.
The real AI-channel demand, isolated and joined to your analytics, plus the raw run data to re-run or model. The feed is yours.
Per-engine share trajectory, the competitors named above you, the moves shipping next, and the delta since last month. No new dashboards.
A quarterly, dollar-terms view of where AI demand can offset paid spend, by category and intent - a defensible underwriting line. Incremental, never one-for-one.
Outbound enrichment feed. No SDK, no JS overlay, no vendor in the production codepath - the same surface as ingesting any data feed. Most security reviews already approve the pattern.
Training-data exposure. Verbatim user prompts. Non-clicked citations. Revenue attribution from logs alone (needs a CDP join). We name our limits before your data science team asks.
FAQ
AI Discovery Intelligence is the measurement and engineering of how AI answer engines (ChatGPT, Claude, Gemini, Perplexity) recommend companies in a given category. You start with an AI Visibility Audit: a category-level read of where you are recommended, who is cited ahead of you, and what it costs you - delivered as an output your teams can act on.
SEO optimizes for ranked search results. AI Discovery Intelligence optimizes for the citation behavior of AI answer engines - what they retrieve, cite, and recommend when a buyer asks the engine directly. Different surface, different signal, different engineering.
Standard scope: a category-sized buyer-query set across 4 AI engines, every response captured, plus a read on whether the engines can reach and cite your content, with reliability-tiered findings (HIGH directly observed / MED statistically inferred / LOW industry intel). Enterprise audits extend to first-party AI-channel signal and a quarterly, dollar-terms read on where AI demand can offset paid spend.
We measure citation behavior - what AI engines show users when they answer category queries. We do not measure training-data exposure directly. Anyone claiming to measure how often your company appears in an engine's training corpus is overpromising; that signal is not reliably available externally.
Yes, and we measure it first. We measure your share of AI recommendations across ChatGPT, Claude, Gemini and Perplexity on a locked panel of buyer questions, then execute around a dozen ranked moves per market - deployed by us where you give us site access, drafted for your team to publish where you don't. Every month re-measures against the baseline, so you see whether the work moved the number.
Tell us your brand and market. Written scope and draft questions back within one business day.
Get a proposal