AI visibility case studies
Compare scope, provider provenance, valid denominators, failed-run handling and limits across three published measurement notes.
Explore the case studies →HOWAICITE RESEARCH
Research that starts with a checkable method.
Study questions, source data and explicit limitations. Our first protocol separates being mentioned, being recommended and being cited.
Inspectable evidenceProvider provenanceLimits kept visible
Publication status: three measurement notes published.
The October competitor benchmark discloses HowAICite's 0-of-25 result (0 of 24 in a second wave on October 12 and 0 of 20 in a third on October 23), exact-name competitor signals and five provider failures. The earlier notes remain visible as separate baselines. All report provider-API observations, not consumer-interface results. The larger recommendation–citation benchmark remains a protocol without empirical results.
AI visibility platform benchmark
Ten US-English buyer questions across four provider APIs: 25 valid answers, five disclosed failures and an observation-level CSV.
Read the benchmark →AI visibility industry watch
A source-linked watchlist of measurement platforms, SEO suites and AI-agent/MCP products—with explicit separation between vendor claims and measured evidence.
Explore the industry watch →HowAICite visibility baseline
Ten buyer questions measured on two dates: 20 valid answers, no own-brand mention or own-domain citation.
Read our baseline →Australian HR software baseline
Twenty completed provider-API answers, four excluded failures, explicit denominators and no invented customer relationship.
Read the measurement note →Recommendation–Citation Gap
A predeclared panel, collection rules, annotation definitions and analysis tools for independent review.
Read the protocol →Readiness baseline
Three timestamped HowAICite page audits, with every finding and a raw JSON download.
Inspect the sample reports →Published research assets
| Asset | Question and design | Status |
|---|---|---|
| Evidence Case Studies | Where can readers compare HowAICite's measured baselines, category benchmark and independent product research? | Three studies collected in one hub |
| AI Visibility Competitor Benchmark | Which AI visibility platforms appeared across ten US-English buyer questions and four provider APIs? | Two waves published: 25 and 24 valid answers |
| HowAICite AI Visibility Baseline | Did our own brand appear in ten buyer questions measured twice with one provider API? | Self-measurement baseline published |
| Australian HR Software Baseline | How often did one tracked brand appear in six frozen buyer questions across four provider APIs? | Aggregate observations published |
| Recommendation–Citation Gap | How often is a brand recommended without a source link, or cited without being recommended? | Protocol published |
| Website Readiness Baseline | What can a buyer verify directly from a public page before measuring AI answers? | Observations published |
| Metric Dictionary | How are mentions, recommendations, citations, source frequency and coverage defined? | Definitions published |
Publication standard
Each empirical release must include its panel version, collection dates, surfaces, requested and observed location, model where available, failures, missing observations, annotation process, denominators and limitations. Raw customer evidence is private by default. Public research has a separate review and publication boundary.
HowAICite sells software in the category it studies. That commercial interest is disclosed in every study. We do not select only the answers in which our brand appears or use our own readiness score as evidence that an AI engine recommends us.
Inspect the open research repository, version history and citation metadata on GitHub →
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