Web Cited Research

11 of 600.

We tested 10 funded B2B data and analytics brands for AI search visibility. Across 4 LLMs and 600 buyer-research responses, only 11 mentioned any of the 10 brands. Eight scored zero. Two broke through.

Published May 15, 2026 · Data & Analytics category
11 / 600 LLM responses that mentioned any of the 10 target brands across 5 standardized buyer prompts. Two brands (AirDNA, UserEvidence) account for the 11; the other eight got zero.

The finding in one paragraph

We picked 10 funded B2B data, analytics, and BI companies, ranging from supply-chain forecasting to short-term rental analytics to customer-evidence platforms. We asked four leading LLMs (ChatGPT, Claude, Gemini, Perplexity) five buyer-research questions any data or marketing leader would plausibly type into an AI assistant. Each question ran 3 times per engine for variance. That produced 600 distinct LLM responses. Only 11 of those responses mentioned any of the 10 target brands by domain or by name: 8 mentioned AirDNA, 3 mentioned UserEvidence, and the remaining eight brands appeared zero times. The category is being defined by a different set of brands.

Where each brand landed

Eight of ten scored 0 / 60. Two broke through. The leader (AirDNA) earned 8 mentions out of 60 LLM responses, primarily for short-term rental analytics queries where the category is small and AirDNA is the obvious answer.

Brand Domain Category Score
AirDNAairdna.coShort-term rental market analytics8 / 60
UserEvidenceuserevidence.comCustomer evidence and proof platforms3 / 60
Alloy.aialloy.aiDemand forecasting and supply-chain analytics0 / 60
DatologyAIdatologyai.comAI training-data curation0 / 60
Bobsledbobsled.comCross-cloud data sharing0 / 60
Nodanoda.aiBuilding performance analytics0 / 60
Carpe Datacarpe.ioInsurance carrier analytics0 / 60
Planalyticsplanalytics.comWeather analytics for retail0 / 60
Orbeeorbee.comAuto dealer analytics0 / 60
Predactivpredactiv.comOnline consumer interest data0 / 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 data and analytics buyer prompts.

  1. Blue Yonder82
  2. Mashvisor65
  3. Kinaxis53
  4. Labelbox51
  5. Manhattan Associates36
  6. Scale AI36
  7. Encord36
  8. Crisp36
  9. Circana35
  10. Capterra33
  11. Lokad30
  12. Demand Solutions30
  13. Prodigy30
  14. Cleanlab30
  15. Trustpilot30
  16. ToolsGroup30

Mention counts are bolded-product-name references across all 600 LLM responses.

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 5 standardized buyer prompts

  1. What is the best demand forecasting platform for CPG and retail brands?
  2. What analytics platforms track short-term rental market trends and pricing?
  3. What tools help AI and ML teams curate high-quality training data?
  4. How do enterprises share large datasets securely across cloud providers?
  5. What platforms do B2B SaaS marketers use to capture verified customer proof and case studies?

The same 5 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. 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.

What this means if your data brand is on the invisible list

Most funded B2B data and analytics vendors have not entered the AI-search conversation. They invested in SEO for category keywords, in webinars, in conference presence, and in product-led growth motions. None of those signals get ingested by LLMs at the speed and scale that determines what AI assistants surface. When a CMO, head of revenue ops, or supply-chain leader asks an AI assistant a category question, the answer comes back with the brands that show up in long-form analyst writeups, in industry-press articles, in Wikipedia entries, and in third-party comparison content.

AirDNA is an instructive outlier here. Their category (short-term rental analytics) is narrow enough that they are effectively the default answer when someone asks an AI assistant about Airbnb-economy data. The other nine brands compete in broader categories where the dominant set of brands has captured the LLM training data through years of third-party content. Catching up requires a deliberate AI-visibility strategy: structured FAQ content that matches buyer-prompt phrasing exactly, schema-marked product pages, and citation footprints on the third-party sites the LLMs index.

Want to know if your data brand is invisible?

Web Cited runs the same measurement against your domain, your buyer prompts, and the engines your data audience actually uses. The Audit comes with a click-to-copy Playbook your engineers ship from in the next sprint. Five business days. Fixed price.

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