Web Cited Research

Zero of 600.

We tested 10 funded B2B compliance vendors for AI search visibility. Across 4 large language models (LLMs) and 600 buyer-research responses, none of them got cited even once.

Published May 15, 2026 · Compliance & Risk Management category
0 / 600 Target-brand mentions across 4 LLMs × 5 prompts × 3 trials × 10 brands. Match criteria: brand domain OR brand name (case-insensitive) anywhere in the response.

The finding in one paragraph

We picked 10 funded B2B compliance and risk-management software companies. We asked four leading LLMs (ChatGPT, Claude, Gemini, Perplexity) five buyer-research questions any compliance officer or VP of risk would plausibly type into an AI assistant. We ran each question 3 times per engine to account for response variance. That produced 600 distinct LLM responses, 296,329 characters of compliance recommendations. None of the 10 brands we measured appeared anywhere in any of those responses, by domain or by brand name. The category is being defined by a different set of brands entirely. Below: who got cited instead, what we measured, and what this means if your name is on the invisible list.

The Invisible 10

All 10 brands score 0 / 60 LLM responses. They were not ranked low. They were not in the conversation at all.

Brand Domain Category Score
360factors360factors.comEnterprise GRC platform0 / 60
AssurXassurx.comQuality management for regulated industries0 / 60
EasyLlamaeasyllama.comWorkplace compliance training0 / 60
RadarFirstradarfirst.comPrivacy incident response and breach notification0 / 60
Evidentevidentid.comThird-party vendor risk and compliance0 / 60
PreVeilpreveil.comCMMC and defense contractor compliance0 / 60
Famafama.ioBackground screening and candidate compliance0 / 60
Cranium AIcranium.aiAI security and governance0 / 60
Archive360archive360.comCompliance data archival and retention0 / 60
F&I Sentinelfisentinel.comAuto dealer F&I compliance0 / 60

Who LLMs cite instead

The same 600 responses cite the brands below by name, repeatedly. These are the companies that have entered the AI-search conversation for compliance buyer prompts.

  1. TalentLMS107
  2. LogicGate73
  3. AuditBoard69
  4. Workiva67
  5. Absorb LMS64
  6. ServiceNow63
  7. Drata62
  8. Domo56
  9. Vanta56
  10. Secureframe49
  11. OneTrust46
  12. KnowBe442
  13. Docebo38
  14. MetricStream36
  15. Compliance.ai33
  16. VComply33

Mention counts are bolded-product-name references across all 600 LLM responses. Several of the top results (TalentLMS, Absorb LMS, Docebo, LinkedIn Learning) reflect citations for the "compliance training" buyer prompt specifically.

Methodology

This study was designed to be replicable. The exact prompts, models, settings, and matching logic are documented below. Anyone with API access to the four engines can re-run it.

Engines tested
OpenAI ChatGPT (gpt-4o-mini)
Anthropic Claude (claude-haiku-4-5-20251001)
Google Gemini (gemini-2.5-flash-lite)
Perplexity (sonar)
Settings (all engines)
temperature 0.2
max_tokens 800
N = 3 trials per (engine, prompt)
Total LLM calls
600
10 brands × 4 engines × 5 prompts × 3 trials
Match criteria
case-insensitive substring match of brand domain OR brand name anywhere in the LLM response, including any cited URLs

The five standardized buyer prompts

  1. "What is the best compliance management software for a mid-market company in healthcare or finance?"
  2. "How do enterprises automate SOC 2 and ISO 27001 audit preparation?"
  3. "What tools do compliance teams use to track regulatory changes and manage policy drift?"
  4. "What is a good employee compliance training platform for a remote workforce?"
  5. "How do companies manage vendor compliance and third-party risk at scale?"

The same five prompts were applied to every brand audit. This is the only way to make a category comparison defensible: if each brand got its own custom prompts, the rankings would reflect the prompt selection, not the brand visibility.

Known limitations of this methodology. Substring matching can miss paraphrased mentions where the LLM references a product without naming the company or domain ("the Predict360 platform" rather than "360factors" or "360factors.com"). LLM responses vary between calls; N = 3 trials per prompt reduces but does not eliminate variance. Buyer prompts evolve; this set reflects May 2026 category language. LLM training data has a knowledge cutoff that may not include recently-launched brands or recent rebrands. None of these limitations explain the 0-of-600 result in this study: it would require a paraphrase miss across every one of 600 distinct responses, which independent inspection of the responses rules out.

What this means if your brand is on the invisible list

Most B2B compliance vendors have not entered the AI-search conversation. They built websites for SEO and email-driven demand, not for LLMs. When a CMO or compliance officer types a category question into ChatGPT, the answer comes back with a different set of brands: the ones that show up in Wikipedia, in long-form analyst writeups, in third-party comparison content, in podcast transcripts, in YouTube reviews. The dominant set is not necessarily better software. It is the set the LLMs have read about most often.

The fix is mechanical. There are specific patterns that earn LLM citations: structured FAQ content matching exact buyer-prompt phrasing, schema-marked product pages, citation footprints on third-party sites the LLMs already index, and content that answers the "vs" and "alternatives to" queries directly. Brands at the top of the cited list above have done some combination of these things, intentionally or not.

The cost of being invisible scales with the share of buyer research that moves from Google to AI assistants. By Gartner's projection, 25% of traditional search volume migrates by year-end. For a B2B SaaS where pipeline starts with category research, an invisible brand loses pipeline that never knew it existed.

Want to know if your brand is invisible?

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