500+
domains in weekly index
4
AI engines tracked
16
LLM crawlers audited
17
AI visibility playbooks
02 / THE SHIFT
Search has split. Your brand needs to win in both.
Half of high-intent research now happens inside generative answers. The mechanics are different: there are no rankings, no SERP, no clicks to optimize. There are citations, omissions, and competitor mentions you cannot see in any analytics tool you already own.
Veezow gives growth, SEO, and content teams the operational layer for AI search — the same way Search Console gave teams the operational layer for Google.
03 / PLATFORM
Four operations. One loop.
Scan
Detect what AI engines cite when they answer questions about your category, brand, and competitors.
Prioritize
Rank fixes by visibility impact — sitemap gaps, schema misses, citation drift, competitor surges.
Monitor
Weekly diffs across answers, citations, and ranking. Alerts when a citation is gained or lost.
Report
Stakeholder-ready PDFs and shareable workspaces, sized for board, exec, or content review.
04 / IN THE PRODUCT
Live signal, not a dashboard for its own sake.
Citation count per engine — notion.so vs two competitors, last 7 days. Demo data.
05 / IN USE
“Veezow is the first tool that actually showed us how AI engines see our brand. It changed how we brief content.”
“The citation matrix changed every board conversation about AI. We could show exactly where we stood versus competitors — in numbers, not vibes.”
“The robots.txt and schema fixes alone moved our Perplexity citations 40%. We'd never have found those without the scan.”
06 / WEEKLY INDEX
What changed in AI search last week.
2026.07.28
Freshness signals: why LLMs cite recently-updated content at higher rates — and how lastmod drives it
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.
2026.07.21
Retrieval-augmented vs. base model citations: why optimizing for the wrong engine delays your results by months
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.
2026.07.14
Schema consistency vs. schema completeness: what actually drives citation accuracy
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.
07 / START