Recently, when we checked the visibility for commercial queries in ChatGPT, most of the sources were not based on listicles. There were more sources that were new while tracking. This triggered us to research whether there had been any new algorithm update from ChatGPT, and we found yes.
“ChatGPT Fan-out queries seems to have pre-selected brand name for finding the sources for citations”
ChatGPT considers a couple of brands (pre-selects) as the authoritative brand for the commercial queries and searches the internet along with those brand names. It was shocking, yet surprising to see the way SEO is getting shaped — not based on content, but on branding and trust.
This research is based on analysis of 200 commercial queries, 12,676 source URLs, 2800+ citations, correlated against 15+ published industry studies.
What has changed in the ChatGPT Algorithm? What happens when ChatGPT Pre-Selects your brand?
From our data, we can see the exact mechanism. When a user types “Best omnichannel customer engagement platform,” ChatGPT generates these fanout queries BEFORE searching the web,
Query 1: “official omnichannel customer engagement platform Braze features”
Query 2: “official omnichannel customer engagement platform MoEngage features”
Query 3: “official omnichannel customer engagement platform Iterable features”
Query 4: “official omnichannel customer engagement platform Insider features”
The model has already decided that Braze, MoEngage, Iterable, and Insider are the brands to evaluate. The web search is verification, not discovery.
This is clearly an algorithm shift or change in the way chatGPT search works. It’s an advantage for a trusted brand as it pre-selects, and it gets more visibility, MQLs, and Pipeline.
In recent times, most of the SaaS marketers reported us during multiple roundtables we had, there is an increase in branded search, and this could be one of the reasons.
This is independently confirmed by Writesonic’s GPT-5.4 study (March 2026, 532 fanout queries analyzed): “GPT-5.4 doesn’t find brands through traditional search. It knows them from training data, then sends domain-restricted queries directly to their websites.”
Even in our GEO checklist, we have mentioned SEO or GEO is not about just content or optimizing in your website, it’s genuine brand and trust building. This ChatGPT new algorithm change proves it again.
The question here is, How do few brands get into that pre-selected list and the others don’t?
The 4 Fanout Query Patterns We Observed
Our research data reveals ChatGPT uses 4 distinct patterns when pre-selecting brands. Your GEO/AEO strategy for ChatGPT should be re-written according to the following patterns.
Pattern 1: “official [BRAND] [category] official”
“official Braze cross-channel engagement platform”
“official Mambu core banking platform official”
“official Oracle FLEXCUBE core banking official”
Decoding this pattern:
The model has a strong [BRAND] ↔ [CATEGORY] association in its internal knowledge. It’s confident enough to name the brand and search for its official site.
Pattern 2: “[domain.com] [category] official”
“plaid.com account aggregation product official”
“stripe.com fintech APIs official”
“m2pfintech.com official banking as a service APIs”
Decoding this pattern:
The model knows the domain specifically. This happens for brands where the domain name itself has a strong web presence.
Pattern 3: “[BRAND] [category] documentation”
“Cashfree BBPS API documentation”
“Razorpay UPI API official documentation merchant payments”
Decoding this pattern:
The model expects this brand to have technical documentation. This pattern appears specifically for API/technical products.
Pattern 4: “[category] [BRAND1] [BRAND2] [BRAND3]” (multi-brand comparison)
“best AI customer engagement platform 2026 Zendesk Intercom HubSpot Salesforce Service Cloud”
Decoding this pattern:
The model has a comparison set already formed. It considers these brands as direct competitors in the same category.
These are the 4 patterns we found across analyzing 200+ transactional or commercial intent queries of B2B SaaS and FinTech niches. Now, it’s time to deep dive on how to make your brand pre-selected by ChatGPT’s Fan-out query.
What Makes Your Brand “Known” to the Model or Pre-selected?
The current ChatGPT algorithm has two types of results getting mentioned or cited. One is pre-selected in a Fan-out query and the other is discovered from the web. You should know how brands are pre-selected in various niches, and optimize accordingly to standalone.
The Pre-Selected vs Discovered Comparison
For each category, we compared brands that got pre-selected with brands that didn’t. The pattern is consistent.
FinTech Niche
Core Banking – Pre-selected: Temenos, Thought Machine, Mambu, Oracle, Finastra
- All these brands have Wikipedia pages.
- All appear in Gartner Magic Quadrant for core banking.
- All have extensive press coverage in banking trade publications.
- All are publicly traded or have major VC funding rounds covered globally
Core Banking – Discovered (not pre-selected): TCS BaNCS, Finacle
- These are PRODUCT NAMES within larger companies (TCS, Infosys).
- They have less independent brand identity even though the model knows TCS and Infosys, but the sub-brand “BaNCS” has weaker standalone web presence.
- Fewer Wikipedia mentions as standalone entities
Bank Account Verification – Pre-selected: Razorpay, Plaid
- Razorpay: $7.5B valuation, Wikipedia page, massive press coverage
- Plaid: $13B valuation, Wikipedia page, Visa acquisition attempt covered globally
Bank Account Verification – Discovered: Cashfree, Sumsub, iPiD
- Cashfree: $700M+ valuation, thinner Wikipedia presence.
- Sumsub/iPiD: Much less press volume
UPI API – Pre-selected: Razorpay, Cashfree, PhonePe
- All three dominate Indian business media for UPI coverage.
- All have extensive Wikipedia sections about UPI.
- All have massive regional press presence
UPI API – Discovered: Setu, Decentro
- Setu: Acquired by Pine Labs, less standalone brand presence for UPI
- Decentro: Series A startup, much less press coverage
SaaS Niche
Customer Engagement Platforms
This is the richest category in our dataset with 8 queries spanning omnichannel engagement, AI engagement, consumer engagement, customer engagement, cross-channel engagement, personalization, digital engagement, and cross-channel marketing.
The data reveals a clear hierarchy of brand recognition,
Tier 1 - ALWAYS pre-selected (appear in fanout queries for 4-5 engagement queries):
- Braze: Pre-selected in 5 queries. Fanout query format: “official Braze cross-channel engagement platform”, “official Braze customer engagement platform omnichannel messaging website”. The model has the strongest [Braze] ↔ [customer engagement] association of any brand in our entire dataset.
- MoEngage: Pre-selected in 5 queries, averaged Position 3. Fanout query format: “official MoEngage cross-channel engagement platform”. Strong association but never beats Braze.
- Iterable: Pre-selected in 4 queries, averaged Position 3. Fanout query format: “official Iterable cross-channel marketing platform”.
Tier 2 - FREQUENTLY pre-selected (appear in 2-3 engagement queries):
- Insider: Pre-selected in 3 queries (omnichannel, customer engagement, personalization)
- Intercom: Pre-selected in 3 queries (AI engagement, customer engagement, digital engagement)
- HubSpot: Pre-selected in 3 queries (AI engagement, digital engagement, cross-channel marketing)
- CleverTap: Pre-selected in 2 queries (consumer engagement, cross-channel engagement)
- Salesforce: Pre-selected in 2 queries, discovered in 1 (cross-channel marketing, personalization)
Tier 3 - ONCE pre-selected (appear for specific niche queries only):
- Zendesk: Pre-selected only for “AI customer engagement”, but the model associates Zendesk with support/AI, not general engagement.
- Adobe Target: Pre-selected only for “personalization”, but the model associates Adobe with personalization specifically, not engagement broadly.
- Bloomreach, Dynamic Yield, Optimizely: Pre-selected only for “personalization”.
NEVER pre-selected (always discovered through web search):
- Infobip: Ranked at 5 for AI customer engagement, but it was discovered, not pre-selected despite having 13 pages crawled across engagement queries.
- Airship: Ranked at 5 for consumer engagement, and it was discovered despite being a well-known push notification platform.
- Customer.io: Ranked at 5 for cross-channel engagement, yet discovered not pre-selected despite strong developer community presence.
- OneSignal: Discovered and Ranked at 6 for cross-channel engagement.
- Klaviyo: Ranked at 4 for cross-channel marketing. Discovered despite $10B+ valuation and massive e-commerce presence.
Why Braze Gets Pre-Selected 5 Times But Infobip Gets 0:
Braze
- Wikipedia page explicitly says “customer engagement platform,”
- Named as Leader in Forrester Wave for Cross-Channel Marketing.
- Dedicated product pages matching every engagement query variation
- Category-defining content (“What is a customer engagement platform”).
- Proprietary research report (Global Customer Engagement Review)
Infobip
- Wikipedia page describes it as a “communications platform” (different category term)
- Stronger association with CPaaS/messaging than “customer engagement,”
- No Forrester Wave leadership positioning for engagement specifically
Why Klaviyo Gets Discovered But Not Pre-Selected:
- Klaviyo has massive brand awareness and a $10B+ valuation, but the model associates Klaviyo primarily with “email marketing” and “e-commerce marketing”, not as “cross-channel marketing platforms.”
- Its Wikipedia page and press coverage use “email marketing platform” language, not the engagement/cross-channel terms that trigger pre-selection for these queries.
- It only appeared in the one query (“cross-channel marketing”) where the category term partially overlaps with its known positioning.
The Critical Pattern Across All 8 Engagement Queries:
- Pre-selected brands are ranked between 1 – 4 in every single engagement query.
- Discovered brands always appear at Position 5-6 (the bottom of the list)
- 3 of 8 queries had no discovered brands, whereas all the pre-selected brands were mentioned for all 8 queries.
- The ChatGPT model 5.3 or 5.4 treats “customer engagement platform” as a well-defined category with a fixed set of known brands as trusted and pre-selects them.
Key takeaway to be pre-selected:
- The brands that get pre-selected have their brand name associated with their category term across multiple independent source types such as Wikipedia, analyst reports, business press, community discussions, and their own website.
- It’s the mapping across source types that creates the strong association, not any single source.
How to Make Your Brand Pre-selected by ChatGPT?
The Brand-Category Association Model
Based on our research data combined with published findings, here is how the [BRAND] ↔ [CATEGORY] association gets built in training data:
1. Wikipedia (holds the highest Weight for Pre-Selection)
Every brand that gets pre-selected 3+ times in our dataset has a Wikipedia page that explicitly names their category.
- Braze’s Wikipedia page says it’s a “customer engagement platform.”
- Temenos is described as a “banking software company.”
- Plaid is a “financial services company” focused on “account verification and aggregation.”
The Wikipedia association is the strongest because,
- Wikipedia is included in every LLM training dataset.
- It uses consistent, factual language linking brands to categories.
- It’s editable but moderated, so the content has passed quality gates.
- Cross-references and citations within Wikipedia create entity relationships.
What you need to do:
- If your company meets Wikipedia’s notability criteria (genuine PR coverage in reliable independent sources), create a Wikipedia page.
- The opening sentence MUST contain your exact category term. Such as “[Your Brand] is a [exact category keyword] provider”.
- Include citations from independent sources (press articles, analyst/research reports) that also use your category term.
- If you don’t meet notability criteria yet, just focus on having enough independent coverage.
2. Analyst Reports (Gartner, Forrester, IDC, CB Insights)
From our data, the pre-selected brands in enterprise categories (core banking, lending, customer engagement platforms) are almost present in all the Gartner Magic Quadrant or Forrester Wave reports.
- Temenos → Gartner MQ for Core Banking
- Braze → Forrester Wave for Cross-Channel Marketing
- Adobe Target → Gartner MQ for Personalization
- Salesforce → Multiple Gartner MQs
Analyst reports matter because:
- They explicitly categorize brands into market segments.
- They’re widely cited in press release, blog posts, and Wikipedia
- The category language in analyst reports becomes the “official” terminology.
- They create training data mentions like: “Leaders in [CATEGORY]: [Brand1], [Brand2], [Brand3]”
What you need to do:
- Submit your product for consideration in relevant Gartner Magic Quadrant, Forrester Wave, or IDC MarketScape categories.
- Even being listed as a “Niche Player” creates the [brand] ↔ [category] association.
- If major analyst reports are too expensive/competitive, target niche analyst firms: IBS Intelligence (banking), CB Insights (startups), G2 Grid reports.
Create a dedicated page on your site when you’re included: /analyst-recognition/ or /gartner-magic-quadrant/.
3. Business Press Coverage (The Repetition Engine)
The ChatGPT 5.3 and 5.4 model builds brand-category associations through repeated co-occurrence in news articles. When 50 articles across TechCrunch, Reuters, Economic Times, and FinTech Global consistently describe Temenos as ‘the core banking provider,’ that association grows stronger with every mention.
From our data, the pre-selected brands have common press coverage patterns:
- Funding rounds covered with category context: “Mambu, the cloud banking platform, raised $266M…”
- Product launches covered with category framing: “Braze launches new cross-channel engagement features…”
- Industry awards: “Temenos wins IBS Intelligence award for best core banking solution”
- Earnings/business updates: “Razorpay, the Indian payment gateway provider, reported…”
What you need to do:
- Every press release, every media pitch, every interview MUST use your exact category term in the first paragraph
- Don’t say “we’re a SaaS company” — say “we’re a [exact category keyword] provider”
- Target niche industry publications over mainstream outlets such as Express Computer, FinTech Global, BFSI Eletsonline, etc,. One article in Express Computer calling you a “[category] provider” is more valuable than a generic TechCrunch mention that doesn’t use your category term
- Press releases published on your own domain newsroom get cited by ChatGPT 18% of the time (BuzzStream, March 2026). Wire services get cited 0.04% of the time. Build your newsroom, not your wire distribution.
Earn coverage specifically about product capabilities, not just funding. The model cares about “[Brand] is a [category]” statements, not “[Brand] raised $X million”
4. Community Discussions (Reddit, Quora, Stack Overflow)
From the SE Ranking study (129K domains): “Domains with millions of brand mentions on Quora and Reddit have roughly 4x higher chances of being cited.”
From our data, Reddit appeared as a source in only 1 query, but community discussions feed training data at a much higher rate than they appear in real-time search results.
The below image gives a clear idea on which subreddits had the highest win-rate in the SERP.
What you need to do:
- Participate authentically in Reddit subreddits relevant to your category (r/fintech, r/SaaS, r/marketing, etc.)
- Answer Quora questions about your category like “What’s the best [category]?” questions are exactly the prompts people ask ChatGPT
- When your brand is mentioned in community discussions, the training data encodes: “users discussing [category] mention [your brand]”
- Don’t spam or self-promote — community moderators will remove it. Instead, contribute genuine expertise and let your brand association build naturally
- Stack Overflow/GitHub discussions matter for technical or Dev products. Developers mentioning your API in implementation discussions creates strong technical authority signals
5. Review Platforms (Clutch, G2, Capterra, Trustpilot)
From the SE Ranking study: “Domains with profiles on platforms like Trustpilot, G2, Capterra, Sitejabber, and Yelp have 3x higher chances to be chosen by ChatGPT as a source.”
From our data, Clutch was the single most-crawled third-party platform (410 pages across 200 queries). For agency queries, Clutch has acted as the discovery mechanism in ChatGPT.
What you need to do:
- Create and maintain profiles on G2, Capterra, Clutch, TrustRadius, and Trustpilot.
- Actively collect reviews (this reminds these LLMs that the account is live). The review count and rating appear in ChatGPT’s crawled data
- Ensure your G2/Capterra categorization matches your target category keyword exactly.
- These platforms feed training data because they explicitly categorize products: “Top [Category] Software” lists create the brand-category association
6. YouTube (The Underrated Channel)
From our data, YouTube appeared as a source 110 times across 200 queries, the 4th most-crawled third-party platform.
From the Ahrefs study: “YouTube is the most-cited domain in AI Overviews overall and has grown 34% over the past six months.”
What you need to do:
- Create product demo videos with your category keyword in the title: “[Your Brand] — The Best [Category] Platform Demo”
- Create comparison videos: “[Your Brand] vs [Competitor] — [Category] Comparison 2026”
- YouTube transcripts become training data. The words spoken in your video become text that the model reads
- YouTube titles, descriptions, and tags all create brand-category associations
From the SE Ranking study: “Domains with profiles on platforms like Trustpilot, G2, Capterra, Sitejabber, and Yelp have 3x higher chances to be chosen by ChatGPT as a source.”
From our data, Clutch was the single most-crawled third-party platform (410 pages across 200 queries). For agency queries, Clutch has acted as the discovery mechanism in ChatGPT.
What you need to do:
- Create and maintain profiles on G2, Capterra, Clutch, TrustRadius, and Trustpilot.
- Actively collect reviews (this reminds these LLMs that the account is live). The review count and rating appear in ChatGPT’s crawled data
- Ensure your G2/Capterra categorization matches your target category keyword exactly.
- These platforms feed training data because they explicitly categorize products: “Top [Category] Software” lists create the brand-category association
7. Partner Ecosystems and Marketplaces
From our data, HubSpot ecosystem (30+ pages), AWS Marketplace (1 page), and Gartner reviews (6 pages) all appeared as sources.
The SEJ article (April 1, 2026) noted: “Bing evaluates credibility by weighting what others say about your brand more heavily than what your own site claims.”
What you need to do:
- Get listed on wherever your product integrates such as HubSpot Solutions Marketplace, Salesforce AppExchange, AWS Marketplace, Zapier, Pipedrive Marketplace
- These marketplace listings explicitly categorize your product: “[Your Brand] — [Category] Integration”
- Marketplace listings are crawled by both training data pipelines and real-time search
8. Your Own Website (The Foundation)
From our data, ChatGPT uses the keyword “official” in 95% of fanout queries. It’s explicitly searching for your official site. When it arrives, your site must clearly declare what category you’re in.
What you need to do:
- Homepage H1 or primary heading: “[Your Brand] — The [Exact Category Keyword] Platform/Solution/Provider”
- Create a dedicated page at /[exact-category-keyword]/ (the Cashfree pattern)
- Create “What is [your category]?” content (the Braze pattern) — position yourself as defining the category
- Publish original research with data: “[Your Brand]’s Annual [Category] Report”
- Maintain developer documentation at /docs/ for technical products
- Host analyst validation on your site: “/gartner-magic-quadrant/” or “/forrester-wave-leader/”
In nutshell, it’s not about ChatGPT, today’s search engine optimization has moved ahead of your own website, and most of the above brand building factors are covered in our LLMO/GEO implementation checklist.
How to become a brand that ChatGPT Pre-selects for it’s Fan-out Query?
Weeks 1-4
From our data, ChatGPT uses the keyword “official” in 95% of fanout queries. It’s explicitly searching for your official site. When it arrives, your site must clearly declare what category you’re in.
What you need to do:
- Homepage H1 or primary heading: “[Your Brand] — The [Exact Category Keyword] Platform/Solution/Provider”
- Create a dedicated page at /[exact-category-keyword]/ (the Cashfree pattern)
- Create “What is [your category]?” content (the Braze pattern) — position yourself as defining the category
- Publish original research with data: “[Your Brand]’s Annual [Category] Report”
- Maintain developer documentation at /docs/ for technical products
- Host analyst validation on your site: “/gartner-magic-quadrant/” or “/forrester-wave-leader/”
In nutshell, it’s not about ChatGPT, today’s search engine optimization has moved ahead of your own website, and most of the above brand building factors are covered in our LLMO/GEO implementation checklist.
Months 1-6
As your brand value accumulates:
- Third-party mentions on existing roundup posts
- Review platform reviews
- Community discussions mentioning your brand
- YouTube content
- Niche press coverage
Your brand starts appearing more consistently in ChatGPT’s answers. At this stage, you’re building “mention share” — the percentage of responses that include your brand. You’re not yet pre-selected in fanout queries, but you’re being discovered more consistently through web search.
Months 6-18
This is the critical transition point. When OpenAI updates its training data (which happens with each model version like GPT-5, GPT-5.1, GPT-5.2, GPT-5.3, GPT-5.4 all had different cutoff dates), your accumulated web presence gets encoded into the model’s knowledge.
The Superlines research (March 2026) notes: “AI bots visiting high-authority sites almost every other day, suggesting that models are fine-tuning parts of their knowledge graphs more rapidly than in previous years.”
For your brand to transition from “discovered” to “pre-selected,” by this point you need:
- Wikipedia page (or equivalent knowledge base presence)
- At least one analyst report mention
- 20+ press articles using your brand + category term together
- Active review platform profiles with meaningful review counts
- Community discussions mentioning your brand in category context
- YouTube presence with category-relevant content
18+ Months
Brands like Braze (5x pre-selected) and Cashfree (2x pre-selected in fanout, 5x winner overall) have years of accumulated web presence.
Their brand-category association is so strong that the model doesn’t need to search. It has considered your brand as the benchmark for your category.
The old SEO strategy was to Rank for 1,000 keywords. In contrast, the new AI SEO strategy is to become the authoritative around your brand’s top 10 category. This could make your brand visible for 10,000+ queries in LLMs
Dominate ChatGPT Visibility: What to Do First?
Priority | Activity | Training Data Impact | Time to Impact | Cost |
1 | Wikipedia page creation | Highest — direct training data anchor | 6-12 months (including building notability) | Low (but requires genuine notability) |
2 | Analyst report submission | Very High — category classification by authority | 6-18 months | Medium-High |
3 | Consistent press coverage with category terms | High — repeated brand-category co-occurrence | Ongoing, compounds over months | Medium |
4 | G2/Clutch/Capterra profiles + reviews | High — category classification + trust signal | 1-3 months to establish | Low |
5 | YouTube content with category keywords | Medium-High — transcripts become training data | 1-3 months | Low-Medium |
6 | Reddit/Quora category participation | Medium — community validation signals | 2-6 months to build presence | Low |
7 | Partner marketplace listings | Medium — ecosystem categorization | 1-2 months | Low |
8 | Own-site category definition content | Medium — but only works if triangulated by external sources | 1-2 months | Low |
9 | Conference presentations + proceedings | Medium — academic/professional credibility | 3-12 months | Medium |
10 | Digital PR on niche industry publications | Medium — category-specific press | 1-6 months | Medium |
Key Takeaways
Usually, the guides on how to get cited by ChatGPT only speak about page-level optimization such as structure, freshness, content depth. Our research on ChatGPT’s Fan-out query reveals a layer that happens before citation i.e., the brand pre-selection in fanout queries.
This pre-selection operates on a completely different signal set than citation optimization:
- Citation optimization = what your web page looks like (structure, freshness, content)
- Pre-selection = what your brand looks like across the entire web (Wikipedia, press, analysts, reviews, community, and more)
You can have the most perfectly structured, fresh, fact-dense page on the internet, but if ChatGPT doesn’t know your brand belongs to the category, it will never search for your page in the first place. The fanout query “official [BRAND] [category] official” only gets generated for brands the model already recognizes.
The published research tells you how to win the citation game once you’re in the search results. Our research tells you how to get into the search query itself. Still, confused or struggling to get cited in ChatGPT, then book with us for a GEO Services strategy call.
