The Invisible 8: 10 Data & Analytics Brands in AI Search

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	Homepage fixes
AirDNA	airdna.co	Short-term rental market analytics	8 / 60	11
UserEvidence	userevidence.com	Customer evidence and proof platforms	3 / 60	3
Alloy.ai	alloy.ai	Demand forecasting and supply-chain analytics	0 / 60	5
DatologyAI	datologyai.com	AI training-data curation	0 / 60	9
Bobsled	bobsled.com	Cross-cloud data sharing	0 / 60	10
Noda	noda.ai	Building performance analytics	0 / 60	9
Carpe Data	carpe.io	Insurance carrier analytics	0 / 60	7
Planalytics	planalytics.com	Weather analytics for retail	0 / 60	5
Orbee	orbee.com	Auto dealer analytics	0 / 60	12
Predactiv	predactiv.com	Online consumer interest data	0 / 60	5

"Homepage fixes" is the per-brand count of fix recommendations detectable from a single homepage fetch and the brand's robots.txt, llms.txt, and sitemap.xml endpoints. Roughly 25 distinct check categories: server-side rendering, structured-data JSON-LD presence (Organization, Product/Service, FAQPage, BreadcrumbList), OpenGraph + Twitter Card completeness, canonical + meta description + title + h1 health, alt-text coverage, mobile viewport, html lang attribute, robots.txt allow-list for AI crawlers (GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot, Google-Extended, anthropic-ai), and llms.txt + sitemap presence. The full Web Cited audit identifies additional fixes from deeper page-by-page analysis, schema validation, citation-footprint checks, and accessibility scans. "n/a" indicates the brand's homepage blocked our discovery user-agent (a fix in itself: AI crawlers may be blocked too).

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. Even brands at the top of this list have remaining homepage fixes available; AI search visibility is rarely saturated.

Blue Yonder82 mentions8
Mashvisor65 mentions6
Kinaxis53 mentions8
Labelbox51 mentions12
Manhattan Associates36 mentions8
Scale AI36 mentions8
Encord36 mentions9
Crisp36 mentions5
Circana35 mentions7
Capterra33 mentionsn/a
Lokad30 mentions9
Demand Solutions30 mentions5
Prodigy30 mentions12
Cleanlab30 mentions9
Trustpilot30 mentionsn/a
ToolsGroup30 mentionsn/a

Mention counts are bolded-product-name references across all 600 LLM responses. "Homepage fixes" is the per-brand count of fix recommendations from the discovery audit described above. "n/a" indicates the brand's homepage blocked our discovery user-agent.

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

What is the best demand forecasting platform for CPG and retail brands?
What analytics platforms track short-term rental market trends and pricing?
What tools help AI and ML teams curate high-quality training data?
How do enterprises share large datasets securely across cloud providers?
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. Citation Monitor reads those prompts every week and ships a click-to-copy Playbook your engineers ship from in the next sprint, from $49/mo.

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