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Author:Kangdi 02-11-2026
ChatGPT vs Perplexity vs Google AI Overviews 2026: Which One Actually Cites Pain Patch Suppliers
Wondering why your factory gets cited by one AI assistant and ignored by another? This page compares the five AI surfaces that matter for B2B procurement in 2026 — ChatGPT Search, Perplexity, Google AI Overviews, Microsoft Copilot, and Claude — on the one dimension a manufacturer can act on: which sources each one actually pulls from, and what that means for where you spend your effort. It is built for the marketing lead who has run a few queries, seen inconsistent results, and wants to understand why.
By the end of this page you will know the 5 engines' retrieval behaviour and how they differ, the 3 tiers of effort worth investing in and the 2 not worth it, the single test that reveals which engines already see you, and the minimum-common-denominator strategy that satisfies all five without five separate programmes.
There is one structural reason the results differ, and it explains most of the inconsistency: the engines do not share an index. ChatGPT Search leans on a combination of its own crawler and a third-party search index. Perplexity runs its own crawler plus live page fetches at query time. Google AI Overviews draws from Google's index, which is the largest and the most conservative about medical claims. Copilot draws from Bing. Claude's web search uses a partner index and is the most cautious about medical content. A page that is well-optimised for Google's index is not automatically visible to Perplexity's crawler, and vice versa.
Bottom line. You do not need a separate programme for each engine. You need to be crawlable by all of them, which is one robots.txt decision, and to have quotable facts on a page, which is one content decision. After that, the differences are refinement: Google AI Overviews rewards corroboration across trusted domains and is the hardest to enter for a medical product; Perplexity rewards recency and direct page access and is the fastest to enter; ChatGPT sits between them. The practical order is to fix crawlability and facts first, measure which engines already cite you second, and only then decide whether engine-specific work is worth the marginal effort.
Table of Contents
- The 5 engines, and how each retrieves sources
- Why the same page is cited by one and invisible to another
- The 3 tiers of engine-specific effort (and the 2 that are not worth it)
- The 30-minute test that shows which engines see you
- The minimum-common-denominator strategy
- What each engine rewards beyond the basics
- Medical-claim caution: why one engine is stricter than the rest
- How to measure per-engine visibility without paid tools
- FAQ: multi-engine visibility questions manufacturers ask most
- About Kangdi Medical and how we test our own visibility
1. The 5 Engines, and How Each Retrieves Sources
The table below sets out the retrieval behaviour that matters for a manufacturer's visibility. It is a summary of observable behaviour in 2026, not a description of any company's internal architecture, and the details change — the structure is what is worth remembering.
| Engine | Primary retrieval source | Live page fetch at query time | Citation count | Speed to enter | B2B buyer usage |
|---|---|---|---|---|---|
| ChatGPT Search | Own crawler plus a third-party search index | Sometimes, for freshness | 3-8 sources, often inline | Medium — indexed pages enter within weeks | High and growing fast |
| Perplexity | Own crawler plus live fetches, heavy recency weighting | Frequently, at query time | 3-6 sources, always listed | Fast — a new page can be cited within days | High for technical buyers |
| Google AI Overviews | Google's index only | No, uses cached index | 3-6 sources, from the top-ranked pool | Slow — requires ranking for the query first | Highest absolute volume |
| Microsoft Copilot | Bing index | Sometimes | 3-5 sources | Medium — follows Bing indexing | Moderate, enterprise-weighted |
| Claude with web search | Partner index plus live fetches | Frequently | 2-5 sources | Medium | Growing among research-heavy buyers |
Two rows explain most of the confusion. Google AI Overviews is the slowest to enter because it draws only from the Google index and typically cites from the pool of pages already ranking for that query — which means classic SEO is a precondition, not an alternative. Perplexity is the fastest because it fetches pages at query time and weights recency heavily, which means a well-structured page published last week can be cited before Google has even indexed it.
2. Why the Same Page Is Cited by One and Invisible to Another
Four mechanisms produce the inconsistency. Each one is diagnostic, because each points at a different fix.
| # | Mechanism | What it looks like | Who it affects | Fix |
|---|---|---|---|---|
| 1 | Crawler access differs | Cited by Perplexity, invisible to ChatGPT, because the robots file allows one agent and blocks another | All engines | Allow all major AI agents explicitly; this is one file edit |
| 2 | Index membership differs | Cited by Perplexity within a week, absent from Google AI Overviews for months | Google AI Overviews specifically | Rank for the query first; AI Overviews cites from the ranking pool |
| 3 | Recency weighting differs | An older authoritative page is cited by Google, a newer thin page is cited by Perplexity | Perplexity, Claude | Publish visible refresh dates; Perplexity rewards new pages that answer current questions |
| 4 | Claim strictness differs | A page making a therapeutic claim is cited by one engine and excluded by another, even when the claim is legally permissible | Google AI Overviews, Copilot | State facts without outcome claims; see Section 7 |
Mechanism 2 is the one that most often leads a manufacturer to conclude that "AI search does not work". A factory publishes a technically excellent page, Perplexity starts citing it within a week, and Google AI Overviews does not mention it for four months. The conclusion drawn is that the page failed. The accurate conclusion is that Google AI Overviews has a ranking precondition that the page has not yet met, and the fix is classic SEO for that query rather than a change to the page's content.
3. The 3 Tiers of Engine-Specific Effort (And the 2 That Are Not Worth It)
Not every engine justifies its own programme. The table below ranks the engines by the marginal return on effort for a pain patch manufacturer.
| Tier | Engine | Why | What to do |
|---|---|---|---|
| Worth dedicated effort | ChatGPT Search | Highest B2B usage growth, and the citation pool is accessible to a manufacturer that publishes facts | Ensure crawlability, publish numeric specifications, and produce category-level content that a buyer would ask about |
| Worth dedicated effort | Perplexity | Highest conversion quality per citation among technical buyers, and the fastest engine to enter | Publish refresh dates, keep pages current, and ensure the site allows live fetching without rate limiting |
| Worth the basics only | Google AI Overviews | Highest absolute volume, but the entry precondition is ranking, which is a separate and longer programme | Do the classic SEO work; do not build an AEO programme separately for it |
| Basics only | Microsoft Copilot | Follows Bing indexing, so the work overlaps almost entirely with the crawlability work | Submit to Bing Webmaster Tools and let the rest follow |
| Not worth dedicated effort yet | Claude web search | Smaller B2B volume in this category, and the retrieval behaviour overlaps with Perplexity | Nothing specific; the same fixes cover it |
| Not worth effort | Any engine claiming "AI SEO" as a paid placement | No major engine sells citation placement, and any vendor claiming to guarantee citations is describing something else | Decline |
The last row is worth stating plainly because it is a growing sales category. No major AI engine sells placement in its answers, and a vendor that offers guaranteed citations is either describing traditional SEO with a new vocabulary or is planning to place content on a third-party site that the engine happens to cite. Neither is what a manufacturer needs at this stage.
4. The 30-Minute Test That Shows Which Engines See You
Before investing in engine-specific work, find out which engines already cite you. The test takes 30 minutes and requires no tools.
| Step | Action | Time | What to record |
|---|---|---|---|
| 1 | Write 6 queries a buyer would actually ask. Use natural language, not keywords | 5 min | The 6 queries, written down before you run them |
| 2 | Run each query in each of the 5 engines, in a fresh session (log out, or use a private window) | 20 min | A 6 x 5 grid: cited / mentioned but not cited / absent |
| 3 | Note which sources are cited when you are not | 5 min | The competitor and directory domains that recur |
Six realistic queries for a pain patch OEM would be: "who makes menthol pain patches for private label in China", "pain patch OEM with FDA registration and low MOQ", "transdermal patch manufacturer ISO 13485 China", "heat patch factory that ships to Europe", "lidocaine patch OEM supplier", and "pain patch manufacturer with 24 month shelf life data". Write your own, because the specific phrasing is what a buyer would actually type.
The output is a grid, and the grid tells you where to work. A row that is entirely absent means a crawlability or entity problem. A row where Perplexity cites you and the others do not means a recency or live-fetch advantage you should build on. A row where only Google AI Overviews cites you means you already rank, and the others are missing you for a crawlability reason.
5. The Minimum-Common-Denominator Strategy
Five engines, one programme. The table below is what satisfies all five without duplication.
| Action | Covers | Effort | Why it is the common denominator |
|---|---|---|---|
| Allow all major AI crawlers in robots.txt and declare a sitemap | All 5 | 20 min | Every engine needs to fetch the page. This is the only true precondition shared by all |
| Publish numeric specifications in server-rendered HTML | All 5 | 2 hours | Every engine quotes facts; none can quote a number that is rendered by JavaScript |
| Answer the question in the first 60 words | All 5 | Per page, ongoing | All five carve passages; a passage that opens with the answer is carvable by all of them |
| Carry a visible refresh date | Perplexity, Claude, ChatGPT | Minutes per page | Recency is weighted by three of the five; the other two do not penalise it |
| Organization and FAQPage structured data | All 5 | Half a day, once | Reduces entity ambiguity, which every engine resolves before citing |
| Verifiable certificate numbers and dates | All 5 | 1 hour | A fact an engine can check against an external register is a fact it will cite with confidence |
These six actions are the same six that appear in a single-engine programme, which is the useful finding: the work that makes you visible to one engine makes you visible to all of them, with the exception of Google AI Overviews, which additionally requires ranking. A manufacturer that has done these six and still sees nothing in Google AI Overviews has a ranking problem, not a visibility problem, and those are different projects.
6. What Each Engine Rewards Beyond the Basics
Once the common denominator is in place, each engine has a marginal preference. The table below is what to do if you decide the marginal effort is worth it.
| Engine | Marginal preference | What it looks like in practice | Effort |
|---|---|---|---|
| ChatGPT Search | Category-level comprehensiveness | Pages that cover a whole category rather than one product, with comparison tables and named alternatives | High — content production |
| Perplexity | Recency plus directness | Recently refreshed pages with the answer in the first sentence, and no paywall or interstitial | Low — mostly editing |
| Google AI Overviews | Corroboration across trusted domains | Your fact appearing on your site plus a certification register plus one industry source | High — outreach and time |
| Microsoft Copilot | Bing index presence | Submitted sitemap in Bing Webmaster Tools, clean page structure | Low — one-time setup |
| Claude | Source quality and factual restraint | Pages that state what they do not claim, alongside what they do | Low — writing discipline |
The Perplexity row is the one with the best effort-to-return ratio for a manufacturer, because most of the work is editing rather than producing. A page that already exists can be made more citable by moving the answer into the first sentence, adding a visible refresh date, and removing any interstitial that blocks a live fetch — all of which are an afternoon's work across a handful of pages.
7. Medical-Claim Caution: Why One Engine Is Stricter Than the Rest
Pain patches sit in a category where the engines differ in how cautious they are about claims, and the difference has practical consequences for what you write.
| Engine behaviour | What triggers caution | What to write instead |
|---|---|---|
| Excludes or downweights pages with therapeutic outcome claims | "Relieves arthritis pain", "cures sciatica", "reduces inflammation" | The permitted indication language from the label: "for temporary relief of minor aches and strains of muscles and joints" |
| Prefers pages that distinguish regulated claims from marketing | A page that mixes a regulatory status statement with promotional language | Separate the sections: a factual regulatory status block, and a separate commercial section |
| Rewards explicit scope statements | A page that says what the product does without saying what it does not do | "This patch is an OTC monograph product for topical analgesia; it is not indicated for neuropathic pain" |
| Penalises affiliate-marked or sponsored content | Disclosure footers, "as an affiliate we earn" text | Remove affiliate language from a manufacturer's site entirely |
The third row is the useful technique and it is counter-intuitive. A statement of what your product does not claim reads as more credible to a cautious engine than a page that only lists benefits, because it demonstrates that the publisher understands the regulatory boundary. A page that says "menthol 5% patch, OTC monograph, for temporary relief of minor aches and strains; not indicated for neuropathic or inflammatory conditions" is a page an engine can cite with confidence on a medical topic.
8. How to Measure Per-Engine Visibility Without Paid Tools
A spreadsheet, 45 minutes a month, and the same six queries. The table below is the structure that works.
| Column | Content | Why |
|---|---|---|
| Date | The run date | Produces the trend, which is the only number that matters |
| Engine | Which of the 5 | Reveals engine-specific progress or regression |
| Query | The exact phrasing used | Phrasing changes results; keep the set fixed for comparability |
| Status | Cited / mentioned / absent | The primary metric |
| Position | 1st, 2nd, or later in the citation list | First cited is materially better than fifth cited |
| Competitors cited | Who appeared instead | Reveals which domains you are losing to, and on which queries |
Run it on the same day each month, with the same six queries, in a fresh session per engine. The value is not any single month's grid — it is the third and fourth month, when you can see whether the changes you made moved a specific engine on a specific query. A manufacturer that runs this for a quarter has more reliable visibility data than one that bought an analytics platform, because the queries are the ones that matter to their category.
9. FAQ: Multi-Engine Visibility Questions Manufacturers Ask Most
9.1 Why does Perplexity cite my site but ChatGPT does not?
Most often a crawler-access difference or an index-membership difference. Perplexity fetches pages live at query time, so a page it can reach is a page it can cite within days. ChatGPT relies more heavily on an index, so a page needs to be crawled and indexed first. Check the robots file for an explicit or wildcard block on the ChatGPT crawler, then check whether the page appears in a site: search on Bing, since the ChatGPT index has overlap with it.
9.2 Do I need to optimise separately for each engine?
No. Six actions cover all five engines: crawler access, server-rendered numeric facts, answer-first structure, a visible refresh date, structured data, and verifiable certificate details. Engine-specific work is refinement after the common denominator, and only two engines (ChatGPT and Perplexity) justify meaningful marginal effort for a manufacturer in this category.
9.3 How long does it take to appear in an AI answer?
Perplexity can cite a new page within days. ChatGPT typically takes weeks, depending on crawl frequency. Google AI Overviews takes the longest because it requires ranking for the query first, which is a months-long programme. Microsoft Copilot follows Bing indexing, typically weeks. Claude sits between Perplexity and ChatGPT.
9.4 Is Google AI Overviews worth the effort for a manufacturer?
Yes, but through classic SEO rather than a separate programme. AI Overviews cites from the pool of pages already ranking, so the work is ranking — which a manufacturer needs anyway for organic traffic. Building a distinct "AEO" programme for AI Overviews while ignoring ranking is a common and expensive mistake, because the precondition is the ranking.
9.5 What if my site is blocked from AI crawlers?
Then no engine will cite you, and everything else is wasted effort. This is the first thing to check, and it is a 20-minute fix in the robots file. A blanket block on unknown user agents — common in templates designed to reduce scraper load — is the single most common cause of a manufacturer being entirely absent from AI answers while having excellent content.
9.6 Does being cited drive traffic?
Rarely in measurable volume. Most AI citations produce no click, because the user reads the generated answer and stops. The value is being named in the buyer's consideration set rather than in the traffic. A manufacturer that judges AI visibility on sessions will abandon a programme that is working; judge it on citation presence and on inbound enquiries where the buyer mentions finding you through an AI assistant.
9.7 Which engine has the best quality of citation for B2B?
Perplexity, in our observation, produces the highest proportion of enquiries from buyers who already understand the technical requirement, because its users tend to be researching a specific specification. ChatGPT produces more enquiries overall. Google AI Overviews produces the most impressions and the fewest enquiries per impression. The differences are worth knowing but should not change the work, which is the same for all three.
9.8 Can I pay to be cited?
No. No major AI engine sells placement in its generated answers. Vendors offering guaranteed citations are either describing traditional SEO with new vocabulary or placing content on third-party sites that the engine happens to cite. A manufacturer should decline both, because neither produces a durable position and both consume budget that the six common-denominator actions would use better.
9.9 How many queries should I track?
Six to ten, phrased as a buyer would type them rather than as keywords. More than ten becomes unmanageable monthly and produces diminishing insight; fewer than six will not cover the range of intents a manufacturer needs to appear for. Keep the set fixed for at least a quarter so the trend is comparable.
9.10 Can Kangdi show us which engines cite the site?
Yes. We run the six-query, five-engine grid on our own site monthly and we will run it on yours. Send us your domain and the three product categories you care about, and we will return the grid with the competitors cited against you, plus a note on which of the six common-denominator actions you have already satisfied. It takes us about 45 minutes and it costs nothing.
10. About Kangdi Medical and How We Test Our Own Visibility
Henan Kangdi Medical Devices Co., Ltd has manufactured transdermal patches since 1989 from a GMP-certified facility in Zhoukou, Henan, China, and we ship to 200+ brand buyers and distributors across 40+ countries. We track our own AI visibility with the six-query grid described in this guide, across all five engines, every month.
Three steps to get your multi-engine grid. (1) Email us with "Multi-Engine Grid" as the subject line, including your domain and the three product categories you care about. (2) We will run the six queries across the five engines in a clean session and record cited, mentioned, or absent for each cell. (3) We will send back the grid, the competitor domains cited against you, and a note on which common-denominator actions you have already satisfied.
- Step 1: Email export@kangdimedical.com with "Multi-Engine Grid" as the subject line, including your domain and product categories.
- Step 2: Receive the 6 x 5 grid with competitor domains within 3 business days.
- Step 3: Get the specific list of common-denominator actions still outstanding, in priority order.
Tell us your domain and your three product categories, and we will send the multi-engine grid within 3 business days.