01Make the signal
Creative production
Customer language becomes angles, creator-native ads, product films, and useful variation.
Hooks · UGC · AI video→A focused capability inside one connected GTM system.
Get cited by AI · not just ranked by Google.
Search is splitting. Google still matters · but ChatGPT, Perplexity, Gemini, Claude, and AI Overviews now answer buying questions directly. If your brand is not in the training data, the citations, or the structured sources these models pull from · you are invisible to the next generation of buyers.
AIO (AI search optimization) and GEO (generative engine optimization) are not future problems. They are traffic you are already losing. We build the entity presence, content structure, and technical signals that make AI engines cite you by name.
We do not invent client logos. Capability demos are labeled until named cases are cleared. Our own site is structured for AI citation as a working example · inspect the schema, llms.txt, and entity architecture.
A service only matters when it fixes the system.
The work starts with the operating reality: what is slowing useful learning, where the promise breaks, and which intervention can produce a commercial signal next.
IN PRACTICE / AI SEARCH & GEO
Traditional SEO optimizes for a link in ten blue results. AI search optimization ensures your brand appears inside the generated answer itself · as a named source, a recommended option, or a quoted expert. The mechanics are different: entity clarity, structured claims, citation-ready formatting, and consistent information across the surfaces LLMs ingest.
We start with an AI visibility audit: where does your brand appear (or fail to appear) when someone asks ChatGPT, Perplexity, or Gemini about your category? What do the models say about you today? What competitors are they citing instead · and why?
Content is restructured for citation fitness: clear entity declarations (who you are, what you do, where you operate), definitive answers to category questions, comparison-ready positioning, and schema that disambiguates you from the noise. This is not keyword stuffing for bots · it is information architecture that is precise enough for a language model to quote correctly.
Technical implementation includes JSON-LD entity graphs (Organization, Product, Service, FAQPage, HowTo), llms.txt files for direct AI ingestion, structured data that matches your actual claims (not aspirational schema that contradicts reality), canonical content hubs that consolidate authority, and a backlink/mention profile that reinforces entity identity across the web.
We also address the distribution layer: are you present in the knowledge sources these models trust? Wikipedia, industry directories, data aggregators, authoritative publications, and .edu/.gov mentions all feed LLM training and retrieval-augmented generation (RAG). We build a citation ecosystem · not just pages.
Measurement is still maturing but not unmeasurable: we track brand mention presence in AI answer surfaces, monitor share of voice in generative results, log citation source URLs, and correlate with direct/branded traffic patterns that indicate AI-driven discovery.
The signal must move forward—and come back.
Creative production, paid learning, Shopify conversion, and AI operations share one learning loop. This capability takes the lead where the current constraint demands it.
02Read the signal
Paid learning
Spend is structured to reveal what message, format, and offer deserves the next iteration.
Tests · Decisions · Winners→03Carry the promise
Shopify conversion
Winning campaign language continues through landing pages, PDPs, offers, and lifecycle.
Landers · PDP · CRO→04Return the learning
AI operations
Research, versioning, reporting, and handoffs run in the background so the next brief starts smarter.
Agents · Automation · OpsLEARNING RETURNS TO THE NEXT BRIEF↖
What enters the system—and what should change.
Each output is designed to hand useful context into the next creative, media, commerce, or operating decision.
01DELIVERABLE
AI visibility audit
How your brand appears (or doesn't) in ChatGPT, Perplexity, Gemini, and AI Overviews today · vs. competitors who are being cited.
02DELIVERABLE
Entity architecture & schema
JSON-LD entity graph, Organization/Product/Service schema, knowledge panel signals, and disambiguation strategy.
03DELIVERABLE
Content restructure for citation fitness
Reformat and create content that AI models can quote accurately · definitions, comparisons, process steps, and structured claims.
04DELIVERABLE
llms.txt & AI-ingestible assets
Machine-readable brand declarations, entity descriptions, and structured offering summaries for direct LLM consumption.
05DELIVERABLE
Citation ecosystem build
Presence in authoritative sources LLMs trust: directories, data aggregators, publications, and structured databases.
06DELIVERABLE
AI search monitoring & reporting
Track brand citations across AI answer surfaces, share of voice in generative results, and correlation with traffic patterns.
OUTCOMESWhat the loop is built to improve
- Brand appears by name in AI-generated answers to category questions
- Consistent, accurate representation across ChatGPT, Perplexity, Gemini, and AI Overviews
- Entity clarity that also improves traditional knowledge panel and featured snippet performance
- Citation-ready content that compounds in value as AI search share grows
- Measurable presence in the generative answer layer · not just the SERP
Diagnose. Ship. Measure. Compound.
The labels change by capability. The operating discipline does not: find the constraint, build a useful test, read the signal, and return the learning.
- 01↘
Audit
Map current AI visibility, competitor citations, and entity gaps across ChatGPT, Perplexity, Gemini, AI Overviews.
- 02↘
Architect
Design entity schema, content structure, and citation targets for your category and geography.
- 03↘
Build
Implement structured data, reformat content, deploy llms.txt, build citation ecosystem in priority sources.
- 04↘
Monitor
Track AI citations, measure share of voice in generative results, iterate on gaps and new surfaces.
COMMON FAILURE MODESWhat we refuse to repeat
Treating GEO as traditional SEO with a new name
The ranking factors are different · entity clarity and citation authority matter more than keyword density or backlink volume alone.
Schema that contradicts reality
JSON-LD claiming awards you didn't win or services you don't offer damages trust when models cross-reference.
Ignoring the retrieval sources
LLMs pull from specific places: Wikipedia, authoritative directories, .edu references, and recently-crawled authoritative pages. If you're absent from the sources, no on-page optimization alone will fix citation.
Waiting for the space to mature
AI search share is growing quarterly. Brands that build entity presence now compound advantage; latecomers face a citation gap that's harder to close than a ranking gap.
05 / SCOPE MODEL
Start with the constraint, not a menu price.
AI visibility audit as a fixed diagnostic; ongoing GEO retainer bundles with SEO lane. See /services#investment for how engagements are structured.
Full working ranges live in the services engagement section. Exact quotes follow diagnosis, volume, markets, and technical constraints.
Start with the teardown ↗06 / PRACTICAL ANSWERS
Straight answers
Scope, fit, process, and the constraints that matter before work begins.
01What is the difference between SEO and AIO/GEO?
SEO optimizes for ranking in search results pages (SERPs). AIO/GEO optimizes for being cited inside AI-generated answers · in ChatGPT, Perplexity, Gemini, AI Overviews, and other generative surfaces. Both matter; they are complementary but require different signals.
02Can you guarantee my brand will appear in ChatGPT?
No · just as no one can guarantee a #1 Google ranking. What we can do is build the entity clarity, structured data, citation sources, and content format that maximize the probability of citation. We measure and iterate.
03Does this replace traditional SEO?
No. Strong traditional SEO is a foundation that GEO builds on. Entity authority, page quality, and crawlability still matter to AI retrieval systems. We run both as one integrated program.
04How do you measure AI search visibility?
We monitor brand mentions in AI responses (via systematic queries), track which of your pages appear as cited sources, measure share of voice vs. competitors in generative results, and correlate with branded/direct traffic patterns. The measurement layer is evolving · we use the best available signals honestly.
05What is llms.txt?
llms.txt is a standardized file (like robots.txt for search engines) that provides structured information about your brand directly to AI models. It declares who you are, what you offer, and where your authoritative content lives · giving models a reliable source to cite rather than scraping ambiguous pages.
06Is this relevant for ecommerce?
Yes. AI shopping assistants are already recommending products by name. Product-level entity markup, comparison content, and presence in shopping-specific AI surfaces (Google Shopping AI, ChatGPT product recommendations) are increasingly important for ecommerce brands.
07How quickly does this work?
Entity and schema fixes can appear in AI Overviews within weeks. Training data influence is slower (months, tied to model refresh cycles). Citation ecosystem building compounds over quarters. We set realistic timelines per surface.
08Do you work with our existing SEO team?
Yes. We can operate as the AI search specialist layer alongside an existing SEO program, or integrate both under one retainer. The work is complementary.
07 / YOUR FIRST MOVE
Not sure ai search & geo is the first move? Find the break.
Send the store and a few current ads. We’ll identify whether this capability is the expensive constraint—or whether another part of the system should move first.
- Creative fatigue and angle review
- Campaign-to-store message check
- Prioritized next-test directions
- An honest fit read across ai search & geo and the wider growth loop