Industry research

Foglift Research

Original, fully-sourced research on AI search visibility: quarterly benchmarks, citation studies, and query-fanout anatomies. Free to read, free to cite, with downloadable data.

Turning the research into an operating plan? Start with the AI search visibility software comparison, use the AI search monitoring workflow to track the same engines in production, then review monitoring cadence and engine coverage before choosing a plan. For an engine-by-engine source taxonomy, read the citation content-types report. For an evidence map of where AI engines already cite Foglift, use the Foglift citation map.

2026-06-10 · v2026-06 · 2026-05-26 to 2026-06-10 (13 unbranded buyer-intent prompts x 5 AI engines; 59 usable answers)

Which AI Search Tools Do ChatGPT, Perplexity, Gemini & Google AI Recommend?

Across 13 unbranded buyer-intent questions and five AI engines, Otterly appeared in 58% of usable answers, Profound in 54%, Semrush in 49%, Peec AI and Ahrefs in 36%, and Foglift in the long tail at 3%. ChatGPT named 1.0 tools per answer on average, while Google AI Overviews named 4.8.

Foglift monitored 13 high buyer-intent questions about AI search visibility tools across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Each answer was tagged for tools named or cited and source domains cited. Competitor-named alternatives prompts and Foglift-named prompts are excluded from share-of-voice numbers to avoid bias.

2026-05-24 · v2026-Q2 · 2026-05-18 (375 buyer-intent responses across 75 prompts × 5 engines; 2,583 citations, 1,430 structurally classified)

Five AI Engines, Five Content Diets: A Q2 2026 Citation-Type Breakdown

Across 1,430 structurally classified citations from five production AI search engines, ChatGPT cites the vendor's own first-party site 68% of the time. The other four engines run 46 to 52%. Only Perplexity cites video meaningfully (9.7%, almost entirely YouTube). Community discussion (Reddit, Quora) appears in zero ChatGPT, Claude, or Perplexity citations and only 1 to 2% of Gemini and Google AI Overview citations.

Derived from the Q2 2026 AI Search Citation Benchmark. Each cited URL is tagged with one of fifteen structural domain categories defined in the Day 1 domain taxonomy (vendor first-party, niche publisher hub, listicle / content-farm, business press, tech press, lifestyle media, institutional, video, community UGC, review aggregator, marketplace, personal blog, developer platform, search-engine cache). Hand-classification covered the top-volume domains; the long-tail unclassified portion is reported transparently. Per-engine percentages use the engine's classified subset as denominator. Surfaces the engine-level content-type lens that the Day 2 binary aggregator-vs-vendor view collapsed away.

2026-05-23 · v2026-Q2 · 2026-03-14 to 2026-05-23 (1,386 scans across 344 domains; 311 with a full AEO score)

AEO Readiness Across 311 Websites: The Median Site Scores 46/100

Across 1,386 scans of 344 distinct domains between 2026-03-14 and 2026-05-23, the 311 domains with a full AEO score have a median of 46/100, while the median SEO score on the same population is 86. The 40-point gap is the gap between being ready for Google and being ready for an AI engine. Only 2 of 311 domains clear 80, and the highest score observed is 85.

Aggregated from Foglift's public scan results table (public.scans). Each domain is deduplicated to its most recent scan, then filtered to rows that produced a numeric AEO score. The eight AEO dimensions and parallel SEO and GEO subscores are computed by Foglift's standard scanner. We report distributions only, never per-domain identifiers. Surfaces the readiness-side counterpart to the engine-side Citation Benchmark: how technically prepared the typical scanned domain is to be cited by an AI engine.

2026-05-22 · v2026-Q2 · 2026-05-18 (375 buyer-intent responses across 75 prompts × 5 engines)

The Top 100 Most-Cited Domains in AI Search (Q2 2026)

Across 375 buyer-intent responses from ChatGPT, Claude, Gemini, Google AI Overview, and Perplexity, the top 100 domains account for 32.8% of all citations. The other 67.2% come from a long tail of 1,019 domains. The #1 domain (YouTube, 52 citations, 36 prompts) is cited by only 3 of the 5 engines. Only 12 domains across the entire dataset are cited by all 5.

Derived from the Q2 2026 AI Search Citation Benchmark. Flat domain frequency count across all 375 responses, ranked by total citation count with ties broken first by distinct prompts cited then by engine breadth. Each top-100 entry is annotated with the 15-category taxonomy label, per-engine prompt counts and citation counts, per-intent prompt counts, and top three vertical exposures. The aggregator-vs-vendor lens reuses the parent benchmark's taxonomy and adds first-class lenses for community UGC (Reddit, forums), video (YouTube), institutional (Consumer Reports, .gov, .edu), marketplace, blog platforms, and dev platforms. Surfaces the canonical AI-search authority list and the structural long-tail of the citation distribution.

2026-05-21 · v2026-Q2 · 2026-05-18 (75 buyer-intent prompts × 5 engines; this artifact pairs the ChatGPT and Google AI Overview responses)

ChatGPT vs. Google AI Overview: The Same Prompt, Two Different Webs

Across 75 buyer-intent prompts the average Jaccard overlap between ChatGPT's and Google AI Overview's cited domain sets is 4.1%, and 64% of prompts share zero cited domains at all. The two engines are running on almost completely disjoint slices of the web even when answering the identical question.

Derived from the Q2 2026 AI Search Citation Benchmark. For each of 75 prompts we pair the ChatGPT and Google AI Overview responses and compute domain-set Jaccard similarity, intersection size, and engine-exclusive domains. We also break the per-prompt overlap down by buyer intent and category, and tally global engine-exclusive domains (domains that appear in one engine's universe but never in the other's). Surfaces the structural divide between two production AI-search products built on the same web.

2026-05-19 · v2026-Q2 · 2026-05-18 (375 buyer-intent responses across 75 prompts × 5 engines)

Buyer Intent Reshapes AI Citations: Discovery, Shortlist, and Variation Cite Different Webs

When the buyer's intent shifts from discovery to shortlist to variation, the set of cited domains turns over almost completely. Across 25 verticals the average pair-wise overlap is just 13.4%, and only 2.2 of a typical 50.8-domain union appears under all three intents.

Derived from the Q2 2026 AI Search Citation Benchmark. For each vertical the cited-domain union is computed for discovery, shortlist, and variation prompts independently, then Jaccard similarity is computed between each intent pair within the vertical and averaged across all 25 verticals. A per-intent aggregator-vs-vendor lens reuses the parent benchmark's domain taxonomy. Surfaces three different domain personalities AI engines reach for at each funnel stage.

2026-05-18 · v2026-Q2 · 2026-05-18 (375 buyer-intent responses across 75 prompts × 5 engines)

AI Search Citation Benchmark, Q2 2026

Q2 2026 reference data: 375 buyer-intent responses across 75 prompts × 5 engines. 1,119 distinct cited domains.

Reference dataset: 75 brand-neutral buyer-intent prompts across 25 verticals (tech SaaS, consumer services, CPG/retail) sent to ChatGPT, Claude, Gemini, Google AI Overview, and Perplexity using each engine's grounded production model with live web search. Cited URLs were extracted, normalized to root domains, and aggregated into reference tables (overall top 25, top 10 per engine, top 5 per vertical, citation-type distribution, URL-depth distribution, cross-engine Jaccard, long-tail concentration).

2026-05-18 · v2026-Q2 · 2026-05-18 (375 buyer-intent responses across 75 prompts × 5 engines)

The Foglift AI Citation Map: Four Authority Patterns in Q2 2026

Every industry falls into one of four AI citation patterns. A fifth quadrant, Vendor Hegemony, is empty by design.

Analytical paper introducing the four-pattern framework (Contested Lead, Editorial Capture, SEO Capture, and Open Frontier) that classifies how AI search citation authority is distributed across 25 industries. Interprets the Q2 2026 benchmark data into a leader-vs-challenger strategy matrix for each pattern.

2026-05-18 · v2026-Q2 · 2026-05-18 (375 buyer-intent responses across 75 prompts × 5 engines)

When AI Engines Cite the Reviewer vs. the Brand: A 25-Vertical Split

CPG / retail responses cite the vendor's own site only 22.7% of the time; tech SaaS responses cite it 92.7%, a 70-point gap. Aggregator share runs the opposite direction (80% for CPG, 44.7% for SaaS), reshaping how a brand should think about AI search authority.

Derived from the Q2 2026 AI Search Citation Benchmark. The 15 structural citation-type categories are collapsed into a binary aggregator-family vs. vendor-first-party lens and broken down by category (tech SaaS, consumer services, CPG / retail), all 25 verticals, three buyer intents, and five engines. Shows the largest single structural divide in the dataset: a 70-percentage-point gap between tech SaaS and CPG / retail in how often the vendor's own first-party domain is cited.