ChatGPT, Perplexity, Google AI Overviews, and Claude are reading your work and re-presenting it to your readers. AIVZ shows you which engines cite you, whether the summaries match what you actually wrote, and what to fix when they don't.
You commission a piece. You edit it. You fact-check it. You publish it. Then a reader asks ChatGPT a question your article answers — and ChatGPT replies with a summary.
That summary might be exact. It might paraphrase. It might cite your byline. It might not. It might compress the nuance you fought for in the editing pass into a single sentence that loses the qualification. Or it might confuse your authoritative source with a less-authoritative one and serve the wrong attribution.
You don't know. Nobody on your team knows. You don't have a system for finding out.
A Perplexity citation is worth knowing about. An AI Overview presence is worth knowing about. Today, the only way to find out is manual prompt testing — slow, partial, not repeatable.
A summary that captures the spirit of a 2,000-word feature is one outcome. A summary that distorts a quote or merges two opposing views is another. Editorial accountability requires knowing the difference.
Some content is structurally hard for AI engines to extract — long narrative pieces, content gated behind subscription walls, articles without clear answer blocks. Knowing which pieces don't surface, and why, is operationally important.
AIVZ is the first vendor-grade system that answers all three.
Track where your content shows up — Perplexity, Google AI Overviews, and a growing set of monitorable surfaces. Per-engine, per-URL, with a citation log that updates on schedule. This is the metric content teams have been hand-rolling with prompt-by-prompt manual testing. Now it runs automatically.
Learn about citation monitoringOf the 93 factors AIVZ measures, the subset most relevant to content teams covers answer-block structure, summary density, citation formatting, definition extractability, and entity disambiguation. Each piece of content gets scored on these specifically — not the whole technical stack. You see which articles are AI-citation-ready and which need a structural pass before they will be.
See the Citation Core 11When AIVZ suggests a fix — adding a summary block, restructuring a heading, exposing FAQ content — the suggestions are designed to be additive, not replacement. Your editor's voice doesn't get rewritten by an AI. The structure underneath the prose gets adjusted so AI engines can extract it cleanly. You ship the same editorial product. The plumbing underneath gets better.
See supported platformsContent teams use AIVZ inside the editorial workflow they already run — between edit and publish, then again on a schedule after AI engines re-crawl.
Editor finishes the piece in the CMS.
AIVZ scores the draft against the Citation Core 11.
Schema, summary block, llms.txt updated automatically.
AIVZ logs every AI engine summary referencing the URL.
If summaries drift, surface the structural fix and re-run.
| Article | Author | Status | AEO Score | Citation Core 11 |
|---|---|---|---|---|
The 2026 Lead-Paint Disclosure Update, Explained |
K. Yamada | Ready | 88 | 11 / 11 |
What Tenants Should Know About Rent-Cap Bills |
D. Okafor | Needs revisions | 62 | 7 / 11 |
Featured: A Year With No Federal Eviction Moratorium |
S. Pham | Drafting | — | — |
Who Pays When the Heat Stops Working? |
R. Lindgren | Ready | 82 | 10 / 11 |
Content teams don't want a tool that fights with the CMS, the editorial calendar, or the existing SEO plugin. AIVZ is built for the same posture as your editorial systems: respect the work, preserve the workflow, fill the gap.
The AEO layer sits on top of how you already work.
AIVZ measures 93 factors across 9 categories. Of those, 11 factors — the Citation Core 11 — drive most of the citation outcome on AI engines. And eight of those eleven are editorial decisions.
| Citation Core factor | What the editorial team controls |
|---|---|
| Front-loaded direct answer | Lead-paragraph clarity |
| Definition density | How well the piece defines its own terms |
| Statistic-source pairing | Whether claims have linked sources |
| Quote attribution | Named-source citation pattern |
| Heading hierarchy | Structural outline discipline |
| Answer block presence | Whether the article surfaces clean Q-and-A blocks where appropriate |
| Summary block | Article-top or article-bottom synthesis paragraph |
| Entity density | Named entities — organizations, people, places, products |
The other three — schema, llms.txt presence, and structural metadata — are technical, and AIVZ generates them automatically. So when content teams ask "do we have to learn schema to do AEO?" — no. Most of what makes content citable is what editorial teams already optimize for.
Or start free — 5 credits, Citation Core 11 only, no card required.
No. AIVZ doesn't rewrite editorial prose. The fix recommendations cover structural changes — adding a summary block, surfacing an FAQ section, marking up entities — not paragraph-level rewriting. Your editorial team retains full control of the writing itself.
AIVZ runs a recurring set of representative queries against AI engines that expose citation data — Perplexity directly, Google AI Overviews where the query is tracked, others where measurable. We log presence, position, and citation accuracy, then surface the results in your dashboard. Frequency and engine coverage scale by tier.
Partially. AIVZ logs the citation and shows the summary text the engine returned. Editorial teams can review and flag inaccuracies. We don't auto-correct AI engines (no vendor can — the engines control their own outputs), but we surface the data so you can take editorial action — issue a clarification, restructure the source content for clearer extractability, or escalate via the engine's feedback channel.
No. Most of what makes content citable is editorial: clear definitions, front-loaded answers, statistic-source pairing, named entities. AIVZ handles the technical schema, llms.txt, and metadata layer automatically on supported platforms. Your team stays focused on the writing.
Yoast and RankMath optimize for traditional search engines — title tags, meta descriptions, canonicals, sitemaps. AIVZ optimizes for AI answer engines — citation extractability, summary blocks, entity disambiguation, answer-engine-tuned schema. They cover different surfaces. AIVZ is companion-not-replacement to your existing SEO plugin and never overwrites it.
Drop in the URL of an article you're proud of — or one you suspect isn't getting the AI traction it should. AIVZ returns a Citation Core 11 score, the structural reasons it does or doesn't surface in AI summaries, and the editorial fixes that would move the score. No card. No signup for the first scan.
