What is GEO (Generative Engine Optimization)?

GEO (Generative Engine Optimization) is the practice of getting AI engines such as ChatGPT, Gemini, Claude and Perplexity to mention and recommend your brand when answering user questions. Unlike SEO, which targets search-engine rankings, GEO targets your visibility, hit-rate and citation position inside AI-generated answers.

Which AI platforms does Maxfound AI monitor?

Maxfound AI connects to the leading international AI engines: ChatGPT, Gemini, Claude and Perplexity. Every scan issues real queries against the live APIs, so results reflect what each engine actually says about your brand.

What is the PAWC metric?

PAWC (Position-Adjusted Word Count) is the core visibility metric from the Princeton ACM KDD 2024 GEO paper (Eq. 3): Imp_pwc = Σ |s|·e^(-pos(s)/|s|) / Σ |s|. It measures how visible your brand name is inside an AI answer, weighting earlier positions more heavily.

What can the AEO rewriting tool do?

The AEO rewriting tool uses real LLMs to restructure your source content into an inverted-pyramid format (headline + direct answer + evidence + context + FAQ) plus JSON-LD, measurably improving how often AI engines cite you.

How do I try Maxfound AI?

You can request a free visibility scan and a product demo. For the full capability list and commercial plans, get in touch via Request a demo.

A coffee chain's AI-channel new customers ×5.4 · a 2-week methodology walkthrough (fictional sandbox)

Sandbox background: why "Yunling Coffee" is big but AI-invisible

To restate: Yunling Coffee is a fictional brand · the scenario and figures below are methodological projections · used to demonstrate the Maxfound AI workflow.

Sandbox setup: Yunling Coffee crosses 20,000 stores, putting its footprint among the category leaders. By common sense, an engine answering "recommend an affordable coffee chain" should mention it first.

But the sandbox scan setup is: across the leading answer engines, for "affordable coffee chain" prompts, Yunling's average visibility is only 45% · consistently behind two leading value competitors.

The problem comes from three places:

Incomplete prompt coverage: in AI training data, "coffee chain recommendations" often pairs with "value" and "affordable," but Yunling's official content emphasizes "signature drinks" and "member discounts" — single-product marketing
Missing store schema: of 20,000 stores, only 12% have complete schema.org structured data (address / openingHours / priceRange) · new-city stores have almost none
Weak new-tier-city signal: in fast-expansion new-tier cities, Yunling grows quickly but content lags · the engine can't pick up the signal that "this city has Yunling"

Week 1 · prompt mapping + AEO rewrite

In the projection, using the Maxfound AI attribution module, we mapped 6 cities × 12 price-sensitive prompts (e.g. "affordable coffee chain in [city]," "good-value coffee shop in [city]") and used pgvector to cluster them into 4 topics:

Value (most heavily suppressed · Yunling coverage 22% · competitor B 81%)
Store density (Yunling's natural advantage · but the content doesn't make it clear · needs to be expressed in numbers)
Product differentiation (Yunling's signature line)
Membership (deep discounts · but expressed too promotionally · engines don't like it)
For the first two topics, we generated 12 FAQ variants and 6 hero rewrites. One worked example:

❌ Original hero: "Yunling Coffee · our signature latte, a nationwide hit"
                    (heavy on sell, no data; the AI can't tell if it's "affordable" or "premium")

✅ Rewritten hero:
"Yunling Coffee · 20,000+ stores nationwide · cups from ¥9.9 ·
 across 300+ cities · an affordable coffee chain that doesn't skimp on quality"

(3 numeric anchors · clearly positioned as "affordable" · easy for an AI crawler to categorize at a glance)

Week 1 · bulk schema.org completion

In the projection, the brand's technical team spent 2 days bulk-filling schema.org data for all 20,000 stores, mainly adding:

{
  "@context": "https://schema.org",
  "@type": "CafeOrCoffeeShop",
  "name": "Yunling Coffee (example store)",
  "priceRange": "¥¥",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "8 Example Road",
    "addressLocality": "Beijing",
    "addressRegion": "Chaoyang"
  },
  "openingHours": "Mo-Su 07:00-22:00",
  "telephone": "+86-400-xxx-xxxx",
  "servesCuisine": "Coffee",
  "hasMenu": "https://example.com/menu"
}

ℹ Tip

Synced at the same time to the relevant map and local-listing platforms, so local-business AI crawlers can accurately identify each store.

Week 2 · multi-platform distribution + crawler signals

Rewriting your own site isn't enough — AI crawlers also need to see consistent signals at other sources. In the projection, using the Maxfound AI AutoMedia module, we synced across 5 platforms:

Community columns: "affordable coffee chain recommendations" · 3 pieces (Yunling vs competitor A vs competitor B) · structured data + store list
Publisher channel: coverage of Yunling's new-tier-city expansion · 6 pieces (one per city) · clearly noting store count + price band
Newsletter: 3 "choosing coffee in new-tier cities" pieces from the official account · fresh training material for AI crawlers
Social UGC: prompts that reinforce "Yunling = affordable chain" (note: all AI-generated content must carry the required AI label and must not fabricate first-hand experience)
Site blog: 5 technical / operational / culture pieces · with schema.org · giving the AI an authoritative source to answer from

Projected result: visibility comparison after 2 weeks (fictional data)

At the end of Week 2, the 7-day re-scan projection (again: fictional data, not measured client results, not a guarantee of outcomes):

Overall AI visibility: 45% → 83% (+38pp)
"Affordable coffee in [city]": from "not in the top 3" → the top slot
Store schema coverage: 12% → 98%
New-user AI referral share (monthly): 2.1% → 11.4% (×5.4)
Secondary-engine visibility: 38% → 76% (+38pp) · 42% → 81% (+39pp)

💡 Key point

The core conclusion of the projection: the new-customer growth curve from the AI Q&A channel can be far faster than the long-cycle accumulation of traditional content channels (fictional projection, not real client feedback).

Five repeatable lessons

Even though the store count in the sandbox is set high, the methodology applies to any chain brand:

Attribute first, act second: don't rewrite the hero straight away · first figure out why the AI overlooks you (the pgvector attribution tool returns results in ~20 seconds)
Numbers beat adjectives: AI prefers concrete numbers like "20,000 stores" and "¥9.9 a cup" over abstractions like "industry-leading" or "premium service" (the latter is also an advertising-law risk zone)
schema.org is the baseline: many chain sites have no schema at all · completing it captures an easy early win
5-platform sync >> a single viral hit: AI crawlers look for consistent multi-source signals · one platform written perfectly is worth less than five platforms all covered
The 7-day re-scan is the KPI: without a re-scan you don't know whether the change worked · Maxfound AI re-scans automatically · don't check by hand

Want this for your brand?

This playbook fits any chain brand with 100+ stores. If you run chain F&B / tea / coffee / convenience / retail / travel / aesthetics / auto, a 2-3 week sprint is worth it.

Check your current visibility in 30 seconds: maxfound.ai/check · it tells you the 4 topics the AI is overlooking you on.