# Veezow

> Veezow is an AI search visibility platform. It measures how AI answer engines —
> ChatGPT, Claude, Perplexity, and Gemini — cite and represent brands in their responses.
> Veezow scans a domain for the signals that determine AI citation probability: robots.txt
> bot access (16 LLM crawlers), structured data (JSON-LD), Common Crawl presence, Wikipedia and Wikidata
> entity coverage, sitemap freshness, and off-site mentions. It scores each signal and
> returns a prioritised action plan.

Site: https://www.veezow.com


## Product

- [AI Visibility Scan — run a free domain scan](https://www.veezow.com
/report)
- [Platform — how Veezow works](https://www.veezow.com
/platform)
- [Solutions — use cases by team](https://www.veezow.com
/solutions)
- [Pricing](https://www.veezow.com
/pricing)

## Company

- [About Veezow](https://www.veezow.com
/company)
- [Customer stories](https://www.veezow.com
/customers)
- [Contact](https://www.veezow.com
/contact)

## Insights — Weekly Visibility Index and editorial

- [Insights hub](https://www.veezow.com
/insights)

### Editions

- [Freshness signals: why LLMs cite recently-updated content at higher rates — and how lastmod drives it](https://www.veezow.com
/insights/editions/2026-07-28) — Domains that update their sitemap lastmod timestamps on a regular cadence are cited 31% more frequently by retrieval-augmented engines than domains with static or absent lastmod values. The freshness signal is measurable, controllable, and largely ignored.
- [Retrieval-augmented vs. base model citations: why optimizing for the wrong engine delays your results by months](https://www.veezow.com
/insights/editions/2026-07-21) — Most AI visibility advice conflates two fundamentally different citation mechanisms. Retrieval-augmented engines (Perplexity, Bing Copilot) respond to on-site changes in days. Base models (GPT, Claude without browsing) require training cycles that run 6-18 months behind. The optimization approach differs entirely.
- [Schema consistency vs. schema completeness: what actually drives citation accuracy](https://www.veezow.com
/insights/editions/2026-07-14) — Completeness without consistency produces a trust penalty. A domain with 12 Organization schema properties that conflict across pages is cited less accurately than a domain with 4 consistent properties. This week's data on the consistency gap and how to close it.
- [The citation stack: which entity layer produces the highest ROI for AI visibility](https://www.veezow.com
/insights/editions/2026-07-07) — After 18 months of citation data, we ranked every intervention by its measured impact on citation probability. Wikidata completeness, robots.txt access, and Organization schema hold the top three positions — by a wide margin.
- [B2B vs B2C citation patterns: enterprise software cited 2.3x more than consumer apps in AI answers](https://www.veezow.com
/insights/editions/2026-06-30) — Enterprise software brands receive disproportionately high citation rates compared to consumer apps of equivalent size. The gap is driven by structured ICP language, job-title-specific content, and deeper entity authority.
- [Prompt sensitivity: why different query phrasings produce different brand citations — and what to do about it](https://www.veezow.com
/insights/editions/2026-06-23) — The same underlying intent, phrased differently, produces different brand citations across AI engines. Brands that understand this pattern can optimize for the query types that drive their highest-intent traffic.
- [Brand hallucination monitoring: how to detect false AI citations and correct them](https://www.veezow.com
/insights/editions/2026-06-16) — AI engines sometimes cite false facts about brands — wrong founding dates, inaccurate product descriptions, fabricated funding history. The root cause is almost always an entity graph gap. Here is how to detect and correct it.
- [Entity disambiguation: how AI engines resolve ambiguous brand names — and how to be the one they pick](https://www.veezow.com
/insights/editions/2026-06-09) — When two brands share a name or operate in overlapping categories, AI engines must choose which one to cite. The decision is determined by entity signal strength, not traffic or ad spend. Here is how to win it.
- [The five queries that determine your AI visibility score — and how to move them](https://www.veezow.com
/insights/editions/2026-06-02) — Every SaaS brand lives or dies on five query archetypes in AI engines: category queries, comparison queries, use-case queries, competitor queries, and brand-direct queries. Each one requires a different fix.
- [Structured data ROI: which schema types actually move citation probability in 2026](https://www.veezow.com
/insights/editions/2026-05-26) — Not all JSON-LD is equal. Analysis of 12,000 AI-cited pages shows that Article, HowTo, and FAQPage schema drive measurable citation lifts — while Product and Review have near-zero effect on AI citation probability.
- [Citation velocity: how long new content takes to appear in AI answers — and what accelerates it](https://www.veezow.com
/insights/editions/2026-05-19) — New content does not appear in AI answers immediately. The lag between publication and citation ranges from weeks to months depending on crawl frequency, entity graph strength, and distribution channels.
- [Reddit as citation infrastructure: AI engines cite community threads 3.1x more than brand pages](https://www.veezow.com
/insights/editions/2026-05-12) — Reddit threads outperform brand-owned content as citation sources across all four major AI engines — with the gap widest on high-intent comparison queries.
- [Citation concentration: top 3 brands capture 67% of AI citations per category](https://www.veezow.com
/insights/editions/2026-05-05) — AI citation share follows a winner-takes-most pattern more extreme than organic search — most tracked brands receive near-zero citations even in categories where they rank organically.
- [Perplexity citation share shifts: SaaS brands lead over 4-week slide](https://www.veezow.com
/insights/editions/2026-04-28) — Citation density in Perplexity answer pages fell 11% across the top 50 tracked SaaS domains.
- [ChatGPT cites Wikipedia-backed brands 2.3x more than unverified sources](https://www.veezow.com
/insights/editions/2026-04-21) — Wikipedia presence correlates directly with citation probability across all four engines.
- [Schema.org/Organization gaps cost finance brands 18% citation share](https://www.veezow.com
/insights/editions/2026-04-14) — Missing sameAs and founder fields suppress citations in Gemini answer pages.
- [GPTBot access improves for 62% of Fortune 500 domains after robots.txt audit push](https://www.veezow.com
/insights/editions/2026-04-07) — Following increased awareness, the share of GPTBot-blocked enterprise domains dropped from 38% to 23%.
- [Reddit AMAs and HN Show posts drive 3.4x citation lift in Claude answers](https://www.veezow.com
/insights/editions/2026-03-31) — Community content from earned Reddit and HN threads consistently appears in Claude citations.
- [Common Crawl inclusion rates diverge by TLD: .io domains lag .com by 22%](https://www.veezow.com
/insights/editions/2026-03-24) — Analysis of 10,000 domains finds systematic CC coverage gaps for .io and .co domains.
- [Wikidata structured entity coverage predicts AI citation probability at 78% accuracy](https://www.veezow.com
/insights/editions/2026-03-17) — Wikidata items with sameAs, founded, founder, and industry fields are cited at nearly double the rate.
- [Gemini citation patterns favor longer-form content over product pages](https://www.veezow.com
/insights/editions/2026-03-10) — Gemini draws from blog posts and case studies 3x more than homepage or pricing pages.
- [Finance brands show highest citation volatility: +/− 12 points week-over-week](https://www.veezow.com
/insights/editions/2026-03-03) — Financial services see the most citation movement of any tracked category — creating both risk and opportunity.
- [Sitemap freshness signal: last-modified dates improve LLM indexing by 31%](https://www.veezow.com
/insights/editions/2026-02-24) — Domains with consistent sitemap lastmod timestamps show significantly higher LLM crawler revisit rates.

### Playbooks

- [Wikipedia presence strategy](https://www.veezow.com
/insights/playbooks/wikipedia-presence-strategy) — How to build a legitimate, citation-grade Wikipedia entity page — notability, reliable sources, neutral tone, and the maintenance process that keeps it from being deleted.
- [Wikidata entity graph](https://www.veezow.com
/insights/playbooks/wikidata-entity-graph) — Wikidata is the structured knowledge source that LLMs cite directly. A complete entity record — sameAs, founded, founder, industry, HQ — correlates directly with citation inclusion.
- [Earned Reddit and HN presence](https://www.veezow.com
/insights/playbooks/earned-reddit-and-hn-presence) — AMAs, Show HN posts, and community discussions on Reddit and Hacker News are among the most-cited sources in Claude and Perplexity. How to earn (not spam) that presence.
- [Common Crawl coverage audit](https://www.veezow.com
/insights/playbooks/common-crawl-coverage-audit) — Common Crawl is the pretraining substrate for most LLMs. Whether your domain appears — and how recently — shapes your base citation probability before any other optimization.
- [Structured data for LLMs](https://www.veezow.com
/insights/playbooks/structured-data-for-llms) — JSON-LD Organization, Article, Product, and FAQPage schema give LLMs machine-readable facts. The specific fields that matter most for each engine — and which patterns to avoid.
- [Citation laundering defense](https://www.veezow.com
/insights/playbooks/citation-laundering-defense) — How adversarial citation manipulation works — and how to detect when a competitor is using it against you. The signals, the monitoring approach, and the legitimate counter-strategy.
- [robots.txt and LLM crawler access](https://www.veezow.com
/insights/playbooks/robots-txt-for-llm-crawlers) — The 16 LLM bots you need to audit access for — including the ones that most teams miss. The right access pattern, the common mistakes, and when allowing access is not enough.
- [FAQPage schema and answer-engine content](https://www.veezow.com
/insights/playbooks/faqpage-schema-and-answer-engine-content) — FAQPage markup lets LLMs pull your answers verbatim for question queries. How to identify the right questions, write answer-engine-ready responses, and implement the schema correctly.
- [Reddit monitoring and competitive intelligence](https://www.veezow.com
/insights/playbooks/reddit-monitoring-and-competitive-intelligence) — How to monitor Reddit for brand mentions, competitor threads, and citation opportunities — and build a weekly cadence that surfaces actionable intelligence before competitors do.
- [Citation velocity and crawl acceleration](https://www.veezow.com
/insights/playbooks/citation-velocity-and-crawl-acceleration) — How to reduce the lag between publishing new content and seeing it cited in AI answers — the crawl chain, what creates delays, and the specific actions that compress the timeline.
- [Press and earned media as citation accelerators](https://www.veezow.com
/insights/playbooks/press-and-earned-media-as-citation-accelerators) — A byline in TechCrunch does more for AI citation probability than 50 blog posts. How press coverage creates citation pathways, which publications have the highest Common Crawl frequency, and how to target them.
- [LinkedIn company page for AI visibility](https://www.veezow.com
/insights/playbooks/linkedin-company-page-for-ai-visibility) — LinkedIn is the primary professional entity signal that AI engines use to validate company identity. A complete, well-structured LinkedIn company page adds an sameAs anchor that lifts citation confidence across all four engines.
- [Crunchbase profile and entity authority](https://www.veezow.com
/insights/playbooks/crunchbase-profile-and-entity-authority) — Crunchbase is a primary entity anchor for company identity in AI training data — especially for B2B and tech brands. A complete Crunchbase profile adds a high-CC-frequency structured entity page that reinforces citation probability.
- [GitHub organization presence for developer tool brands](https://www.veezow.com
/insights/playbooks/github-organization-presence) — GitHub is indexed by Common Crawl at near-daily frequency and appears in AI training data as a high-authority entity source. For developer-tool and infrastructure brands, a complete GitHub org profile is a primary AI visibility signal.
- [Product Hunt launch for AI citation authority](https://www.veezow.com
/insights/playbooks/product-hunt-launch-for-ai-citation) — Product Hunt pages are among the highest-CC-frequency .com pages for tech products — a well-executed launch creates a permanent, high-authority citation source that compounds over time.
- [G2 and Trustpilot reviews as AI citation signals](https://www.veezow.com
/insights/playbooks/g2-trustpilot-reviews-as-citation-signals) — Review aggregator pages on G2, Trustpilot, and Capterra carry disproportionate weight in LLM product queries. How to build review authority that compounds citation probability.
- [YouTube channel for AI visibility](https://www.veezow.com
/insights/playbooks/youtube-channel-for-ai-visibility) — YouTube transcripts are indexed by Common Crawl and cited by Perplexity and Gemini. How to structure channel presence, video metadata, and transcripts to maximize citation probability.

## Legal

- [Privacy Policy](https://www.veezow.com
/legal/privacy)
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/legal/terms)
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/legal/cookies)