How do you actually measure AI search visibility?

We run a recurring set of category and brand queries against ChatGPT, Perplexity, Gemini, and Google AI Overviews, log whether and how your brand is cited, and track that citation rate over time the same way an SEO tool tracks keyword rankings.

Does this replace traditional SEO?

No, it layers on top of it. The schema, entity clarity, and answer-block structure that get you cited by AI systems are the same fundamentals that strengthen classic organic rankings, so the two compound rather than compete.

AI search visibility (AEO/GEO)

You rank on page one of Google, but ask ChatGPT, Perplexity, or Google's own AI Overview about your category, and your brand never comes up. That's a different search engine with different rules, and most agencies claiming to do this can't show you a citation-rate number.

Start a project Build a Package

Examples of the work

Placeholder
AI answer citation
Placeholder
Answer-block page
Placeholder
Citation tracking
Placeholder
Visibility walkthrough

What we do

We get brands cited inside AI Overviews, ChatGPT, Perplexity, and Gemini answers, not just ranked in classic blue links, by structuring content, schema, and entity signals around how these systems actually select what to cite.

Every agency now claims to do 'AEO.' The credible version tracks your brand-mention and citation rate over time, the same way SEO tracks rankings. We build self-contained answer blocks, a full schema graph tying your entity together, and original data points that give these systems a reason to cite you instead of a competitor.

What you get

Citation-rate tracking, not vague promises about 'AI visibility'
Content restructured for semantic completeness, the trait AI systems cite most
Placeholder
A validated schema graph tying every page back to one clear entity
Placeholder
Reporting tied to citations and AI-referred traffic, not just rankings
Placeholder

How it runs

Baseline current citation rate across ChatGPT, Perplexity, Gemini, and AI Overviews
Restructure priority pages around self-contained, semantically complete answer blocks
Build and validate a full JSON-LD schema graph across the site
Re-test citation rate on a recurring cadence and report movement

Work and examples

A walkthrough of this service in action, and the case studies where it did the work.

Placeholder

Related case studies

See all work

Capabilities

Be the answer, not just a ranking

AI answer engines choose what to cite differently from how search ranks pages. These are the pieces that make your brand easy to cite.

Citation tracking
Brand mentions and citations tracked across ChatGPT, Perplexity, Gemini and AI Overviews.
Answer blocks
Direct answers in the first ~100 words, complete without a click-through.
A schema graph
Organization, Service, FAQ, Breadcrumb and Article markup tied into one entity.
Entity and authority audit
Your core categories checked for clarity and topical depth.
Original data points
Facts worth citing, so these systems have a reason to choose you.
Recurring re-tests
Citation rate re-measured on a schedule, with the movement reported.

Outcomes

Brands cited inside AI Overviews see meaningfully higher organic and paid click-through rates on the same queries versus uncited competitors. We'll track and report your own citation movement rather than quote industry averages as your results.

To verify

Nine-brand content system

One content system, nine brands, zero chaos.

Each brand in the portfolio produced content independently, with wildly inconsistent quality, cadence, and no shared learning between teams.

01
9
brands on one system
All nine brands publishing on a consistent weekly cadence
Approach
Designed a single modular content system adaptable to each brand's voice
Reported results
Percent change across our case studies
faster mobile load time
Ratnadeep Homes
62%
less time per asset
Nine-brand content system
55%
lower CAC
Sugar Muktam
41%
off-season revenue lift
Northline Retail
33%
Each bar is a different metric from a different engagement, drawn on a 0–100% scale. It shows the percentage reported in that case study and is not a ranking of clients.
55%
less time per asset
Production time per content asset cut significantly through shared templates
Approach
Built reusable briefing templates that cut production time per asset
1
shared performance loop
Cross-brand learnings now feed every new brief instead of starting from zero
Approach
Centralized performance data so what worked for one brand informed the rest

Trust

What you can count on

The principles behind every engagement, and what is specific to this one.

01
Show the math
Every recommendation ties back to a number we can defend. No vanity metrics, no black boxes.
02
A number you can track
We track your citation rate over time the way SEO tracks keyword rankings.
03
Layered on SEO
The same fundamentals strengthen classic organic rankings, so the two compound.
04
Your results, not averages
We report your own citation movement rather than quoting industry averages.
05
Build systems, not one-offs
A campaign ends. A system compounds. We default to building the thing that keeps working after we hand it off.