GEO and AEO FAQ: 60 Questions on Getting a Financial Brand Cited by ChatGPT, Gemini, Perplexity and Google AI Mode
Short answer: This page answers 60 questions about generative engine optimisation (GEO) and answer engine optimisation (AEO) for banks, insurers, asset managers, NBFCs and fintechs. It covers what the terms mean, how each AI engine appears to pick its sources, which content formats get cited, the entity and technical foundations, third-party authority, and the compliance and measurement questions specific to BFSI. The answers are written by Yamm Labs, a design-led brand agency for fintech and BFSI companies founded in Gurugram in 2017, which runs GEO programmes for financial brands and is running one on itself in public.
On this page
Yamm Labs is not related to Yet Another Mail Merge (YAMM), the Google Workspace add-on, or to yaM Labs, a US meeting-software startup.
Basics: GEO, AEO, AI Overviews, AI Mode and answer engines
What is generative engine optimisation (GEO)?
Generative engine optimisation (GEO) is the practice of making a brand’s pages, facts and third-party mentions easy for AI answer engines such as ChatGPT, Gemini, Perplexity and Google AI Mode to retrieve, quote and attribute. Where search engine optimisation targets a ranked position, GEO targets a mention or a citation inside a generated answer. For a financial brand it covers three things: the technical and entity signals that tell an engine who you are, the content an engine can lift as a passage, and the third-party sources it trusts when it compares brands.
What is answer engine optimisation (AEO)?
Answer engine optimisation (AEO) is the discipline of structuring content so that a system built to return one answer, rather than a list of links, can use it directly. It predates generative AI and grew out of featured snippets and voice assistants. In practice it means one question per section, the direct answer in the first sentence, short self-contained paragraphs, tables for comparisons and a visible date. AEO is the content-format half of a GEO programme; the entity, technical and authority work sits around it.
What is the difference between GEO and AEO?
The terms overlap and many agencies use them interchangeably. We use GEO for the whole programme: entity consistency, crawler access, schema, citable content, third-party authority and monthly measurement across six engines. We use AEO for the narrower content-format discipline inside it: writing pages that answer a question in a liftable passage. A brand can do AEO well and still be invisible if registries, directories and reviews say nothing or say the wrong thing; that gap is what GEO addresses.
What are Google AI Overviews?
Google AI Overviews are the AI-generated summaries that appear at the top of some Google search results pages, above the classic links, with a small set of cited sources. They are generated for a subset of queries, most often informational and comparison queries. For a financial brand the practical points are that the Overview draws on pages that already rank, that it favours pages with a direct answer near the top, and that it can name brands and cite Google Business Profile information for local intent.
What is Google AI Mode?
Google AI Mode is a conversational search mode inside Google Search in which the whole results page is an AI-generated answer with follow-up questions, rather than a summary above links. It uses Google’s index, its knowledge graph and Google Business Profile data, and it shows cited sources and, for local prompts, business cards with ratings and review counts. In our Day 1 baseline it was the engine most likely to surface competitors’ Business Profiles and to blend unrelated companies with similar names.
What is an answer engine?
An answer engine is any system that takes a question in plain language and returns a synthesised answer rather than a ranked list of pages. ChatGPT with search, Perplexity, Gemini, Google AI Mode, Microsoft Copilot and Claude with search all fit the description. Each retrieves a small set of sources, reads them, and writes a response that names brands and cites pages. Because the answer is short, it names far fewer brands than a classic results page, which is why being one of them matters.
How is GEO different from SEO?
SEO earns a ranked position on a results page; GEO earns a mention or citation inside a generated answer. Three differences follow. Answers cite three to eight sources, so there is no page two. Engines lift passages, so a page’s structure matters more than its total length. Engines trust third-party agreement, so a directory or listicle that names you can outweigh your own homepage. Classic rankings still feed Google AI Mode and Copilot, so SEO remains an input to GEO, not a rival to it.
Does GEO replace SEO?
No. Google AI Mode and AI Overviews draw on Google’s classic index and rankings, and Copilot and ChatGPT draw on Bing’s. A page that cannot be crawled or does not rank for anything is unlikely to be retrieved by an engine either. What changes is the goal of the work: instead of optimising for a position, you optimise for being quoted accurately. Most technical SEO hygiene carries over; keyword-stuffed pages and thin location pages do not, because an engine has no reason to lift them.
Which AI engines matter for a financial brand in India?
We track six: ChatGPT, Google Gemini, Perplexity, Google AI Mode (including AI Overviews), Microsoft Copilot and Claude. Google AI Mode matters most for mass retail intent because it sits inside Google Search. ChatGPT and Perplexity matter for research-led buyers and journalists. Copilot matters inside corporate Windows and Office environments, which is where many BFSI procurement and treasury teams work. Gemini matters for Android and Workspace users. Claude is smaller but is used heavily by product and technology teams.
How long does GEO take to show results?
A monthly re-query is the honest unit of measurement. Technical and entity fixes can change an engine’s answer within weeks once the source is re-crawled; third-party authority takes longer because it depends on other people publishing. Our own programme is planned in three 30-day pillars and measured at day 30, 60 and 90, with targets set at baseline. Anyone who quotes a fixed number of weeks for a brand to appear in ChatGPT is guessing; the engines do not publish refresh schedules.
How each engine picks its sources
The answers in this section are Yamm Labs’s working model, built from reading the citations behind our own baseline answers and the engines’ published crawler documentation. None of the vendors publish their ranking logic and all of them change it. Treat these as a practitioner’s map, not as vendor-confirmed facts.
How does ChatGPT choose which sources to cite?
Our working model, not a vendor statement: ChatGPT with search retrieves through the Bing index, and its citations lean on review platforms, Wikipedia and Wikidata, and comparison pages it can quote more than once. In our Day 1 baseline it cited one competitor’s listicle three times in a single answer and used Google Business Profile cards for a city prompt. The implication is to be indexed in Bing, hold consistent entity data on the registries and review sites it reads, and appear in comparison content.
How does Perplexity choose sources?
Our working model: Perplexity weights freshness and passage-level structure. It tends to cite recently updated pages and to lift specific sections under clear sub-headings rather than whole articles. Dated pages with a visible “last updated” line, question headings and short answer paragraphs are easier for it to use. It also runs its own crawler, PerplexityBot, so robots.txt must allow it. We could not test Perplexity logged out on Day 1 because it showed a sign-in wall, so our baseline for it is pending.
How does Google AI Mode choose which brands to name?
Our working model: Google AI Mode draws on classic Google rankings, existing listicles and Google Business Profile data. For “best agency” prompts it named the brands that appear in ranking listicles and on those brands’ own sector pages. For city prompts it showed Business Profile cards with star ratings and review counts. For brand-fact prompts it read LinkedIn, Crunchbase and similar stubs, and it merged two companies with similar names. Rankings, listicle inclusion and a correct Business Profile therefore all matter.
How does Gemini decide what to say about a company?
Our working model: Gemini weights consistency across sources and Google’s knowledge graph, and it reads the brand’s own website closely. In our baseline it described Yamm Labs accurately and positively from yammlabs.com, but it did not name the founder and it repeated a wrong city from a company registry, because the site itself did not state either fact plainly. For discovery prompts it leaned almost entirely on a single competitor listicle. The fix is to state the facts on the site and correct the registries.
How does Microsoft Copilot find sources?
Our working model: Copilot reads the Bing index and responds to the signals Bing uses, including Bing Webmaster Tools submission and IndexNow pings. A site that only manages Google Search Console can be well indexed in Google and thinly indexed in Bing, and Copilot will reflect that. Bing Places is the equivalent of Google Business Profile for local intent. We could not test Copilot logged out on Day 1 because of a sign-in wall, so our Copilot baseline is pending the next check.
How does Claude search the web?
Our working model: Claude with search uses Anthropic’s own search index and fetcher rather than Google’s or Bing’s. It needs the main content to be present in the HTML, a working sitemap, and robots.txt rules that allow ClaudeBot and Claude-SearchBot. Our technical gate on Day 1 confirmed Claude-SearchBot receives an HTTP 200 from yammlabs.com. Claude showed a sign-in wall for logged-out use on Day 1, so it is measured from the first monthly re-query onward.
Why do AI engines cite listicles so often?
When a prompt asks for “best” or “top” anything, the engine is constructing a comparison, and a page that already compares several brands matches the shape of the answer it needs. A brand’s own homepage is a single data point; a listicle is a ready-made answer with names, one-line descriptions and often a ranking. In our Day 1 baseline, four of Gemini’s five citations for one prompt were the same listicle. Getting into good third-party lists is therefore a core deliverable.
Why do different engines name different agencies for the same prompt?
Because they retrieve from different indexes, weight sources differently and sample differently. For “best fintech branding agencies in India” our baseline showed ChatGPT, Gemini and Google AI Mode agreeing on a core of three or four names that appear in the same listicles, then diverging on the rest. Engines also vary between sessions. That is why we score a prompt across six engines and count Present results rather than trusting any single answer.
Do AI engines use Google Business Profile?
Google AI Mode does, visibly: for city and category prompts it shows Business Profile cards with the business name, star rating and review count, and it named agencies with 4.4 to 5.0 ratings and between 9 and 103 reviews in our Day 1 baseline for a Gurgaon prompt. ChatGPT also surfaced Business Profile cards for a city prompt. A profile with the wrong category, no reviews or a stale address is a visible weakness, which is why review count is one of our tracked measures.
Do AI engines read Wikipedia and Wikidata?
Our working model is that ChatGPT and Gemini lean on Wikipedia and Wikidata for entity facts such as founder, founding year and headquarters, because those sources are structured and widely mirrored. Most financial brands below the largest listed companies will not qualify for a Wikipedia article, and we do not recommend forcing one. A Wikidata item, where the brand meets its notability requirements and the facts can be referenced, is a lower bar and helps engines resolve the entity.
Content that gets cited
What is answer-first structure?
Answer-first structure means the page states its answer before its argument: a heading phrased as the question, a first paragraph that answers it in plain declarative sentences, then the detail, evidence and caveats. Engines lift passages, and the passage they lift is usually the one that answers the question most directly. On our pages this takes the form of a short answer block under the H1 and question-phrased H2 headings, each followed by a paragraph that stands on its own.
What is a passage-level section?
A passage-level section is a block of a page, typically a heading and 80 to 200 words, that makes sense on its own when quoted without the rest of the page. Engines retrieve and cite at that level, not at page level. To write one, restate the key noun in the section instead of using “it” or “this”, keep one idea per section, and avoid references to “above” or “below”. A reader landing on that section alone should understand it.
Do tables help a page get cited?
Yes, for comparison and specification content. A table with a clear header row gives an engine a structured set of facts it can quote or summarise without inferring relationships from prose. Comparison tables (brand versus brand, option versus option), scorecards and step lists are the formats we see cited most often. Keep tables simple: plain HTML, one fact per cell, a caption or heading that says what is being compared, and no images of tables.
Why do dates matter for AI citations?
Engines that weight freshness, Perplexity in particular, use visible dates to decide whether a page is current, and users see the date in the citation. A page with a clear “last updated” line and a dated byline is easier to trust than an undated page, and a dated page you keep updating gives the engine a reason to re-crawl it. The four citations Gemini used for one of our baseline prompts all pointed at a listicle carrying a publication date of 6 March 2026.
Do bylines and author pages matter?
Yes. A named author with a role, a profile page and consistent details across the site and LinkedIn gives an engine a person entity to attach the content to, and it supports the experience and expertise signals that Google describes as E-E-A-T. For financial content the author’s role matters more than their fame: a page signed by a product head or a compliance lead reads differently from an unsigned page. We put a byline on every page and link it to a founder or author page.
Do FAQ pages get cited by AI engines?
They do, when each question is a real buyer question and each answer is self-contained. An FAQ page is a set of passage-level sections by design, and FAQPage schema tells engines exactly which text answers which question. Weak FAQ pages fail because they answer questions nobody asks or give two-line answers that say nothing. We write 50 to 100 word answers that restate the key noun and can be quoted alone, and we group them by the stage of the buying decision.
Should a financial brand publish its own listicle?
Yes, with care. A brand-published “best agencies” or “top fund houses” page will be cited by engines if it is genuinely useful and includes competitors fairly; a list that only names the publisher is ignored. For regulated brands the page must avoid performance rankings and must carry the required disclaimers in the same passage as any comparison. The stronger play is to be named in other people’s lists, but publishing a fair one of your own is a legitimate first step.
What is a comparison page and why does it get cited?
A comparison page sets two or more options side by side on the same criteria: brand versus brand, product versus product, in-house versus agency, and so on. Engines cite them because a “which should I choose” prompt is a comparison, and a page that already holds the criteria and the verdict is the easiest source to lift. A good comparison page names when the alternative is the better choice; one that always concludes in the publisher’s favour is less likely to be trusted or quoted.
How long should a citable page be?
Long enough to answer the question completely and no longer. Engines cite passages, not word counts, so a 900-word page with six tight sections can outperform a 4,000-word page with the same answer buried in paragraph forty. Our service pages run 1,600 to 2,200 words because they answer eight to ten buyer questions each; our FAQ hubs are longer because they hold 40 to 120 questions. What matters is that each section is complete on its own.
Should we write for the engine or the reader?
For the reader, in a structure the engine can use. Everything that helps an engine lift a passage, a question heading, a direct first sentence, a table, a date and an author, also helps a reader who is scanning. What does not help either is keyword repetition, invented statistics or marketing adjectives; engines summarise those away and readers distrust them. The test we apply is whether a compliance reviewer and a busy CMO would both accept the page as accurate and useful.
Entity and technical foundations
What is Organization schema and why does it matter for GEO?
Organization schema is a block of structured data, usually JSON-LD, that states a company’s name, URL, logo, founder, founding date, address, contact details and profile links in a form machines read directly. It gives engines a single canonical record of who the brand is. It matters most when other sources disagree: a clear Organization node on the site is the reference point. One block per site is the rule; our own homepage had two conflicting blocks on Day 1, which we replaced with one.
What is sameAs in schema?
sameAs is a property inside Organization or Person schema that lists the URLs of the same entity elsewhere: the LinkedIn company page, Instagram, Facebook, Wikidata, Crunchbase and similar. It tells an engine that those profiles describe the same company as the website, which helps it merge signals correctly and avoid blending the brand with a similarly named company. For a brand with a name collision, a complete sameAs list is one of the cheapest disambiguation fixes available.
Should a financial brand have a Wikidata entry?
If the brand meets Wikidata’s notability requirements and the facts can be referenced to reliable sources, a Wikidata item helps engines resolve the entity: founder, founding date, headquarters, industry and official website in a structured form. It is a lower bar than a Wikipedia article and is often read by the same systems. It should not be created with unreferenced claims or promotional text; a Wikidata item that gets reverted or flagged is worse than none.
How does Google Business Profile affect AI answers?
Google AI Mode shows Business Profile cards, with ratings and review counts, for city and category prompts, and it appears to use the profile’s category, address and description when deciding whether a business fits the prompt. A profile with the wrong primary category, an old address or no reviews is a visible weakness. For a BFSI brand with branches, each verified location contributes. We track review count and rating as a measure precisely because AI Mode displays them.
What is llms.txt?
llms.txt is a plain-text file at the root of a website that gives AI systems a short, curated summary of the site and links to its most important pages, in the way robots.txt gives crawlers rules. It is a proposed convention rather than a standard, and no engine has confirmed how it uses the file. We publish one because it costs little, it is a clean place to state the canonical entity facts and page excerpts, and it will be useful if adoption grows.
Which AI crawlers should robots.txt allow?
For a brand that wants to be cited, allow the search-and-citation bots: OAI-SearchBot (ChatGPT search), PerplexityBot, Claude-SearchBot and ClaudeBot, Bingbot (which Copilot and ChatGPT rely on) and Googlebot. Blocking them removes the brand from the answer; it does not protect anything a public website is not already showing. Training-only crawlers such as GPTBot and Google-Extended are a separate decision. Our Day 1 gate confirmed OAI-SearchBot, PerplexityBot and Claude-SearchBot all receive HTTP 200 from yammlabs.com.
What is Google-Extended?
Google-Extended is a robots.txt token that lets a site opt out of having its content used to train Google’s AI models. It does not affect Google Search indexing or, as far as Google has stated, whether a page can appear in AI Overviews or AI Mode, which are governed by ordinary Googlebot rules. A brand can therefore block Google-Extended for training reasons and still be cited in AI Mode. We check the token during the technical gate and leave the decision to the client.
What is IndexNow?
IndexNow is a protocol through which a website pings participating search engines, including Bing, whenever a page is added, updated or removed, instead of waiting to be re-crawled. Because Copilot reads Bing and ChatGPT search retrieves through it, IndexNow is the fastest way to get a new or corrected page in front of those engines. Most WordPress SEO plugins support it with a key. We treat Bing Webmaster Tools plus IndexNow as the Bing-side equivalent of Google Search Console.
Does page speed or JavaScript rendering affect AI citations?
Rendering matters more than speed. Many AI fetchers read the raw HTML and do not execute JavaScript the way Googlebot does, so content that is only injected client-side, or hidden inside a builder’s shortcodes, may be invisible to them. The fix is to ensure the main article text, headings, tables and schema are present in the served HTML. Speed matters at the margin because slow or erroring responses waste a fetcher’s budget, but a fast page with no readable content still earns nothing.
What is an entity home page?
An entity home page is the page an engine treats as the authoritative description of the company: usually the About page, sometimes the homepage. It should state the canonical facts in plain prose, in the first screen: name, what the company does, founding year, founder, city, and, where names collide, what the company is not. Our Day 1 audit found yammlabs.com had no About, founder or Contact page, which is one reason three engines gave three different cities; we published all three that day.
Third-party authority
Why do third-party listicles matter more than our own pages?
Because a “best” or “top” prompt is a comparison, and an engine constructing one prefers a source that already compares several brands. Your own page is one data point; a third-party list is an answer. Listicles also carry an independence signal a brand page cannot. In our Day 1 baseline, the brands named across three engines for a fintech branding prompt were the brands that appear in the same two or three listicles. That is the mechanism we are working with.
Do directories like Clutch and DesignRush influence AI answers?
Agency directories such as Clutch and DesignRush publish category and city listings, ratings and reviews that engines can read, and they often rank for “top agencies in” queries, which feeds Google AI Mode and Copilot. Being listed with accurate details, the right categories and verified reviews gives engines an independent source that agrees with your site. Whether a paid or sponsored placement moves an engine is unproven; a free, accurate, reviewed listing is the baseline we recommend.
How do Google reviews affect AI recommendations?
Google AI Mode shows Business Profile cards with star ratings and review counts for local and category prompts, so reviews are directly visible in the answer. In our Gurgaon baseline the agencies shown had between 9 and 103 reviews. Reviews also give engines quotable language about what a business does. We set a review target at baseline and pursue it through genuine requests to past clients, never through incentives or bulk services, which violate platform rules and expose a regulated brand to risk.
Does press coverage help GEO?
Yes, when the coverage states the facts an engine needs. A profile or interview that names the founder, city, founding year and specialism in a reputable publication gives an engine a citable, independent source. Coverage that only mentions the brand name in passing helps less. For a BFSI brand, trade press, regulator-adjacent publications and business dailies matter more than volume. Old or wrong press is also a liability: one of our Day 1 entity errors traced to a 2019 profile.
Do Reddit and Quora affect AI answers?
Community threads are cited by ChatGPT, Perplexity and Google for experience-led and “is X any good” prompts, because they read as first-hand opinion. For a regulated brand, the useful posture is to answer questions accurately under a named account and correct wrong facts, not to seed promotional threads, which are usually detected and removed. We monitor threads that name the brand and treat them as sources to remediate when they carry wrong facts.
Does LinkedIn content influence AI engines?
The LinkedIn company page and the founder’s profile are read as entity sources: Google AI Mode cited a LinkedIn stub for our head-office city on Day 1, and got it wrong because the stub was wrong. LinkedIn articles also appeared among the citations for a GEO agency prompt. So the company page must carry the correct facts, the founder’s experience entry must match, and long-form posts on LinkedIn can be cited. Engagement numbers matter less than accuracy.
How do we get into a competitor’s or publisher’s listicle?
Identify the lists engines are already citing for your prompts, then check each publisher’s inclusion process. Some accept a short submission with a description and proof of work, some update on request, some are pay-to-play, and some are a competitor’s own page and will never include you. We prioritise independent publishers and directories, provide accurate copy they can paste, and log every request. Targets are set per quarter; three third-party listicles is our own 90-day goal.
Should we pay for directory listings or sponsored listicles?
Sometimes, and with eyes open. A paid listing on a directory the engines already cite can be worth it if the listing is accurate and carries reviews. A sponsored slot in a “top agencies” post on an unknown blog is usually not, because the engine has no reason to trust the page. We check whether the page already appears in citations before any spend, and we never pay for a placement that implies an independent ranking. For a regulated brand, disclosure rules may also apply.
How many third-party mentions does a brand need?
There is no published threshold. What our baselines show is that the brands engines name consistently appear in two or three of the same listicles, hold a reviewed Business Profile and have consistent registry data. We set counts as targets rather than promises: for our own programme, three third-party listicles, 15 Google reviews and eight of ten source corrections in 90 days. The monthly re-query then shows whether those counts moved the presence score.
What is source remediation?
Source remediation is the work of correcting third-party pages that state wrong facts about the brand, because engines repeat them. On Day 1 our own list included a data vendor listing the head office in Palo Alto, a company registry site giving a Ghaziabad address, and a LinkedIn experience entry with the wrong city and dates. Each item gets an owner, a correction request and a status. It is slow and unglamorous, and it is where most entity errors in AI answers come from.
GEO for BFSI brands specifically
How does SEBI, IRDAI or RBI compliance apply to AI answers?
The rules attach to the source content, not to the engine. SEBI governs how mutual funds and AMCs communicate, IRDAI governs insurers, and RBI governs banks and NBFCs. If a page or a third-party mention would need a disclaimer, a risk statement or approval as an advertisement, it still needs it when the aim is to be quoted by ChatGPT. What changes is that the engine summarises, so the compliant version must survive summarisation. We work with the client’s compliance reviewer on every page.
Where should disclaimers sit on a page for GEO?
In the same passage as the claim they qualify. An engine lifts a section, not a page, and a disclaimer in the footer or a separate box may be dropped from the lift. If a paragraph describes a fund, the market-risk statement belongs in that paragraph in plain words. If a section compares insurance products, the statement that benefits depend on policy terms belongs in that section. Repeating a short disclaimer several times reads oddly to a writer and correctly to an engine.
Can a fund or insurer publish performance claims for AI engines to quote?
We advise against leading with them. A hedged performance statement, once summarised by an engine, tends to become an unhedged one, and the brand remains responsible for what was quoted. Content that engines can safely cite for a regulated brand is category education, process explanation, comparison on features rather than returns, and entity facts. Where regulation permits and the client’s compliance team approves a performance disclosure, it must carry its disclaimers inside the same passage.
What is a brand-name collision and how do we fix it?
A brand-name collision is when an engine merges your company with another that shares part of its name. Yamm Labs is not related to Yet Another Mail Merge (YAMM), the Google Workspace add-on, or to yaM Labs, a US meeting-software startup, yet on Day 1 Google AI Mode answered a “Yamm Labs reviews” prompt with Trustpilot and G2 pages for the mail-merge tool. The fix is a plain disambiguation line on the About, Contact and FAQ pages, complete sameAs links, and consistent facts everywhere.
What is share of answer?
Share of answer is the proportion of tracked prompt-engine runs in which the brand is named, and how prominently, compared with competitors. We measure it as the count of Present results out of the matrix total, broken down by query type, and we record which other brands were named in each answer so the client can see who holds the share instead. It is the AI-answer equivalent of share of voice, with the caveat that answers vary between sessions.
What is a monthly re-query?
A monthly re-query is the re-run of the full prompt matrix, 20 prompts across six engines from a logged-out session on an India IP, on the first working day of every month, with each result scored Present, Partial or Absent and the brands and sources recorded. It is the unit of measurement for the programme. Results are compared with the baseline and the previous month, the scorecard is regraded, and the next month’s actions are chosen from what did and did not move.
Why can no agency guarantee placement in an AI answer?
Because the engines control the retrieval and the writing, and they change both without notice. Sources, models and sampling vary between engines, between sessions and between weeks; a competitor can publish a better page tomorrow; a registry can revert a correction. An agency controls its deliverables, its cadence and the honesty of its measurement, and that is what Yamm Labs commits to. Anyone offering a guaranteed position in ChatGPT or Google AI Mode is selling something they cannot control.
What does a Present, Partial or Absent score mean?
Present means the brand is named in the answer and described correctly. Partial means it is named with an error, such as the wrong city or a merged identity, or appears only in a citation without being named in the text. Absent means it does not appear. Each prompt-engine pair gets one score per re-query. Our own Day 1 baseline was Absent on 12 of 12 discovery runs and Partial, with entity errors, on both brand-fact prompts.
How should a BFSI brand handle wrong facts in an AI answer?
Trace the citation, then fix the source. Engines rarely invent an entity fact; they repeat one from a registry, a directory, a press profile or a LinkedIn stub. Correct the source, state the right fact plainly on your own About page and in Organization schema, add sameAs links, and, where the engine offers a feedback control, report the error. Then re-query the following month. For regulated brands, keep a log of wrong statements and corrections in case a customer relies on one.
How does Yamm Labs run GEO for BFSI brands?
Yamm Labs, a design-led brand agency for fintech and BFSI companies founded in Gurugram in 2017, runs GEO in three 30-day pillars: technical and entity foundation, citable content, then third-party authority, followed by a monthly re-query. Progress is graded on a six-category scorecard and a 20-prompt, six-engine matrix. We are running the same programme on yammlabs.com and publishing the log. The service page explains the deliverables in full.
Want the programme behind these answers?
Read the AI visibility (GEO/AEO) service page for BFSI brands, follow the public 90-day log, or send us five prompts your buyers would type and we will tell you what the engines are citing instead of you. Talk to Yamm Labs →
Last updated: 18 September 2026