Vertical Intelligence — Complete
Standard plus AI citation analysis across ChatGPT, Claude, and Gemini.
Vertical Intelligence Complete is the full Vertical Intelligence Standard package — 200 keywords, SERP composition, content gaps, buyer archetype — plus a parallel AI citation analysis: each keyword probed across ChatGPT, Claude, and Gemini with the cited URLs captured and scored.
The output adds an AI visibility baseline and a wedge-query list — the keywords where AI engines are quoting somebody specific and that somebody could be displaced. Effect-size statistics document which surface-level features predict citation in your vertical at standard confidence thresholds.
Built for marketing leaders treating AI search as a real channel, not a curiosity.
Are AI engines quoting us — or competitors — when buyers ask?
What buyers actually ask.
Are AI engines actually citing brands in my vertical, or am I early?
The report tells you in numbers. We probe up to 200 keywords across three frontier engines and report citation density per keyword — not as a vibe but as a quantified surface. If your vertical is below the citation floor, the report says so explicitly so you do not over-invest in AI search work.
Which AI engine should I optimize for first?
The deliverable disaggregates citation behavior by engine. ChatGPT, Claude, and Gemini have measurably different citation patterns; the report shows you which engine is most consequential in your vertical and why. The wedge-query list narrows that further to the queries where displacement is realistic.
How do you actually probe the AI engines?
Each keyword is rewritten as a buyer-language question and submitted to ChatGPT, Claude, and Gemini through their API surfaces. The cited URLs and the surrounding text are captured, normalized, and scored. The raw probe transcripts are available on request.
How is this different from running my own ChatGPT prompts?
A handful of manual prompts cannot disentangle citation pattern from prompt phrasing or session noise. The report runs probes at scale across three engines, controls for prompt and session, and reports effect sizes — not anecdotes.
What is a "wedge query"?
A keyword where AI engines are reliably citing a specific competitor URL whose authority signal is thin enough that displacement is statistically realistic. The wedge-query list is the prioritized intervention input — most clients work it as a project list with their content team.
How long until citation patterns shift after a content change?
In our white paper data, observed median citation drift on contested keywords is in the four-to-twelve-week range. The Complete report is the diagnostic baseline; a separate Citation Displacement Brief or Citation Drift Atlas is the right product if you need the displacement playbook or quarterly drift tracking.
Will this work for non-English verticals?
The probe layer supports six languages. Citation density tends to be lower in non-English verticals, which we surface in the report as a methodology note rather than as a confident finding when sample size is insufficient.
The deliverable, in detail.
- Everything in Vertical Intelligence Standard — 200-keyword universe, SERP composition, content-gap inventory, buyer archetype — delivered against the same statistical thresholds.
- AI citation analysis across ChatGPT, Claude, and Gemini. Each keyword carries a per-engine citation density, a citation-share read, and the cited URLs identified by domain and content type.
- Effect-size statistics predicting citation. Surface-level features (length, schema, entity density, freshness) are scored against citation outcome in your vertical at standard confidence thresholds, with directional and validated tags applied.
- Wedge-query list and AI visibility baseline. The wedge list is the prioritized intervention input; the baseline is the per-keyword citation read your team will track against future work.
How the report is built.
Vertical Intelligence Complete combines the Standard data stack — 200-keyword universe, SERP snapshots, community-language samples — with a parallel probe layer. Each keyword in the universe is rewritten as a buyer-language question and submitted to ChatGPT, Claude, and Gemini through their respective API surfaces.
The probe captures cited URLs, surrounding citation text, and engine metadata for every probe. The raw probe corpus is normalized, deduplicated, and joined back to the keyword universe so each keyword carries a citation density per engine and a citation-share read across the engines.
A senior analyst reviews the joined dataset before drafting. Effect-size statistics are computed for the surface-level features that predict citation in your vertical (length, schema markup, on-page entity density, freshness signal) at standard confidence thresholds. Findings are tagged validated or directional accordingly.
The Counter-Signal Pass is included on every report. We surface community pushback, negative-language clusters, and counter-evidence to any thesis the report names so the recommendation arrives with its strongest opposing case attached.
The full methodology is documented in our public white paper, The State of AEO and GEO in 2026, which is also the methodology proof for the citation-probing technique.
Counter-Signal Pass is included on every report. The full Foragentis methodology is documented in The State of AEO and GEO in 2026.
What this report does NOT do.
Procurement-grade reports scope themselves. The work below is adjacent and important — and is not in this SKU.
Vertical Intelligence Complete does not predict citation outcomes after content changes — it identifies where citation is happening, who is being cited, and which surface features correlate with citation, but the report does not promise that any given content intervention will succeed.
The probe layer is a snapshot, not a tracking series. If your team needs quarterly drift tracking on a 500-keyword portfolio, Citation Drift Atlas is the right product. If you need a ranked target list of competitor URLs to displace rather than a portfolio diagnostic, Citation Displacement Brief is the right product.
AI engine behavior changes between model releases. Findings are stamped with a model-version footer. We do not promise stability of any specific finding across a model upgrade and we say so on the page.
What the engagement costs.
The Counter-Signal Pass — every thesis stress-tested against its strongest opposing case — is included on every report at no extra cost. See the Counter-Signal block on the catalog hub →
Read the published sample.
About Foragentis.
Foragentis is an AI research and product company based in Sacramento, California. ForIntel is the business-intelligence research arm — producing custom dossiers across four buyer lanes: Search & AI Visibility, Markets & Locations, Capital & Innovation, and Specialty.
Every claim in a ForIntel report traces to a public source. Findings are re-verified before delivery. The Adversary/Analyst architecture pairs a senior analyst with a counter-signal pass on every thesis. Anything below our statistical thresholds is labeled directional rather than validated.
Methodology is documented in The State of AEO and GEO in 2026 — a 9,900-word, 42-page public study with effect-size statistics across four frontier AI engines.
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