Formula Won Labs

[ Generative Engine Optimization ]

Get Cited by ChatGPT, Gemini, Claude, and Perplexity

Customers ask AI for local recommendations now. If your business doesn't show up, you're invisible on a surface that didn't exist three years ago. We fix the Google Business Profile and source data those engines read from, restructure your site for direct citation, and track your visibility weekly so you know exactly where you stand on every engine. GEO builds on a solid Google Maps foundation; if you don't have that yet, we start there.

How AI Finds Local Businesses

Google Business Profile

Not appearing in local search results

✕
AI

ChatGPT

Can't recommend what it can't find

G

Gemini · Siri · Perplexity

Blocked at source

Fix the source. The rest follows. Every AI assistant pulls local business data from Google. Rank there, and you appear everywhere.

Your Google profile powers

Google Maps

reads your profile, decides ranking

ChatGPT

reads Google, decides who to recommend

Gemini / Siri

reads Google, powers iPhone search

If your profile is weak, Google hides you. AI ignores you. Competitors get the call.

Visibility Radius

AI ready
You rankCompetitor ranks

Expand your Google Business reach

Get found by AI search

[ Why This Matters ]

SEO ranks pages. GEO gets your business cited.

4

AI engines now influence local business decisions: ChatGPT, Gemini, Claude, Perplexity

~3,000

monthly searches on AI engines for "chatgpt seo" alone, 5x the Google volume for the same term

0%

is where most local businesses sit today on category-level AI prompts. Including ours, before we started.

Traditional SEO optimizes a webpage for Google's blue links. GEO optimizes the entire data chain: your Google Business Profile, citations, schema, and content structure, so the four AI engines can extract you and recommend you when someone asks. The work overlaps with SEO but the deliverable is different: a citation, not a click.

[ The Problem ]

Why most businesses are invisible to AI engines

Customers ask ChatGPT, Gemini, or Perplexity for recommendations and your business does not appear in the answer.

AI engines start from Google. Your competitors get cited because their Google data is cleaner than yours.

Your website is structured for blue-link SEO, not for direct citation. LLMs read it but they do not quote it.

You have no way to measure AI visibility week over week, so you cannot tell whether anything is improving.

Generic AI advice says "add schema" without specifying what, where, or why. That is the easy part. The hard part is the source data.

[ How We Fix It ]

Get cited, not just indexed

We baseline your visibility across all four engines on the prompts your customers actually use, not generic test queries.

We fix the source layer first (Google Business Profile, citations, schema) because every AI engine reads from it.

We restructure your existing pages for LLM extraction: FAQ headings, semantic triples, and list-format answers that get quoted directly.

We build topic pillars on the categories where you have real authority and demand-validated search volume.

We track your visibility every Monday on the same dashboard we run on ourselves. You see movement in real time.

[ Our Own Baseline ]

We measure ourselves before we measure you

Most agencies sell AI visibility without showing their own. We publish ours. As of our latest run, Formula Won Labs is at 6.2% overall visibility across the four engines on 28 demand-validated category prompts, and 100% of those mentions are when our brand name is already in the question. On unbranded category searches like "best local SEO agency" or "generative engine optimization," we're at 0%. Same as you'd be when you start.

We track this every Monday on the same dashboard you'd see for your business. The 90-day plan is to move from 0% to 25-40% on the highest-priority categories. You'll see our weekly numbers move alongside yours.

[ What's Included ]

The full GEO stack, run for you

4-Engine AI Visibility Baseline

We measure your visibility across ChatGPT, Gemini, Claude, and Perplexity on the exact prompts your customers use. The output is a per-engine percentage and a list of which competitors are getting cited instead. We run this on ourselves every Monday and we run it on you the same way.

Source Layer Repair

All four AI engines start from Google. If your Google Business Profile has the wrong category, missing services, thin reviews, or inconsistent citation data, no amount of website work will help. We fix the source first: GBP, directory citations, schema, and review velocity.

Schema Built for LLM Extraction

Service, Organization, FAQPage, and LocalBusiness schema in JSON-LD on every page that needs it. We write properties LLMs actually parse: areaServed, serviceType, hasOfferCatalog, knowsAbout. Not the dump-everything approach that bloats pages and trains nothing.

Topic Pillars That Get Cited

LLMs cite pillar pages with depth, not 50 thin city pages. We identify the 5 to 8 topics where your business has authority, then build pillar pages structured for extraction: clear definitions, comparison tables, FAQ sections, and internal links to evidence pages.

Q&A Content for Direct Citation

Conversational AI quotes content that already looks like an answer. We restructure your existing pages with H2/H3 question headings, semantic triples in the first sentence (subject - verb - object), and list-format answers that LLMs lift verbatim into their responses.

Weekly Tracking Dashboard

Every week your visibility runs again. You see which engines cite you, which prompts you've moved on, and which competitors fell off. Same dashboard structure we use to track Formula Won Labs, so you can see the same trend lines in real time.

[ How It Works ]

Source layer first, then distribution, then tracking

01

AI Visibility Baseline

We pull your current visibility across ChatGPT, Gemini, Claude, and Perplexity on a custom prompt set built from your category, services, and city. The output is a per-engine percentage, the prompts you appear on, and the competitors getting cited instead.

02

Fix the Source Layer

Every AI engine reads Google. We rebuild your Google Business Profile, fix or add citations on the directories LLMs train against, and add schema where it's missing. This is unglamorous work, but it's what moves the needle.

03

Build the Distribution Layer

We pick 3 to 5 topic pillars where you have real authority and demand-validated search volume, then build pillar pages structured for LLM extraction. FAQ blocks, semantic triples, comparison tables, and internal linking. Pages LLMs cite, not pages they ignore.

04

Track Weekly, Adjust Monthly

Every Monday your visibility runs again. We adjust based on what's moving and what isn't, just like we do with our own dashboard. You get a monthly call to walk through the numbers and decide what to push next.

[ Related Reading ]

How GEO actually works

GEO sits on top of strong local infrastructure. Most clients also need GBP management and local SEO running underneath. We scope these together when the audit shows the source layer is the constraint.

Frequently Asked Questions

Generative engine optimization (GEO) is the practice of getting your business cited by AI engines like ChatGPT, Gemini, Claude, and Perplexity when someone asks them for a recommendation. It overlaps with SEO but the deliverable is different. SEO ranks a webpage in the blue links. GEO gets your business name extracted into an AI-generated answer. The work involves three layers: fixing the source data those engines read from (Google Business Profile, citations, schema), restructuring your site for direct extraction (FAQ blocks, semantic triples, pillar pages), and tracking your visibility across all four engines on the prompts your customers actually use.

Traditional SEO optimizes a webpage to rank in Google's blue links. GEO optimizes the entire data chain so AI engines can extract and cite you in a generated answer. The technical work overlaps (schema, content structure, internal links) but the goals are different. SEO measures clicks. GEO measures citations. A page can be invisible in Google's blue links and still get quoted by ChatGPT, and the reverse is also true. Most businesses need both, but the strategies are run separately and measured separately.

We track ChatGPT, Gemini, Claude, and Perplexity on every engagement. These are the four engines that drive the vast majority of AI-influenced search behavior today. We also monitor Google's AI Overviews and Bing's Copilot when they trigger for your category, but the four LLMs are the core tracking surface. Each one is measured weekly on a custom prompt set built from your services, category, and city.

Yes, but the path is different from ranking in Google. ChatGPT and other LLMs cite businesses based on what their training data and live web search results say about you. We get you cited by fixing your Google Business Profile (which all four engines read from), seeding your business across the directories LLMs learned from, restructuring your site so quotable answers are easy to extract, and building topic pillar pages with the depth LLMs prefer. Most local businesses move from 0% AI visibility to 25-40% on category-level prompts within 90 days.

All four major AI engines source local business information primarily from Google. ChatGPT pulls from web search results that lean heavily on Google's index. Gemini is built by Google. Claude and Perplexity both query the live web, which surfaces Google-indexed sources. If your GBP has the wrong category, missing services, thin reviews, or inconsistent NAP data, every AI engine inherits that broken data and either ignores you or describes you incorrectly. We fix the source first because nothing downstream works without it.

Source layer fixes (GBP, citations, schema) start showing up in AI engines within 4 to 6 weeks. Content restructuring and pillar page work compounds over 60 to 90 days. The fastest movement comes from the source layer because LLMs refresh their understanding of businesses through live web searches each time someone asks. The slowest movement comes from the major LLMs' periodic retraining cycles, which is why we focus most of our effort on signals that get re-read on every query.

No. Anyone guaranteeing specific AI engine rankings is misrepresenting how these systems work. LLMs blend training data, live web context, prompt phrasing, and engine-specific weighting in ways no agency controls. What we guarantee is the methodology: a measured baseline across all four engines, a documented source-layer fix, a tracked content build, and weekly visibility reporting on the same dashboard we run on ourselves. The numbers move because the underlying data shifts, not because we tweaked the algorithm.

Yes. As of our latest weekly run, Formula Won Labs gets named 6.2% of the time across the four engines on 28 demand-validated category prompts. Of those mentions, 100% are when our brand name is already in the question. On unbranded category searches, we're at 0%, the same starting point most of our clients have. We measure ourselves every Monday on the same dashboard we run on you, and we publish the numbers as they move. The 90-day plan is to reach 25-40% on the highest-priority categories.

Find out where you're losing calls

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