Which AI engines matter for a financial brand in India?

By Yamm Labs · Updated September 2026 · 6 min read

A salaried customer in Pune types “best term plan for a 35-year-old” into Google and reads an AI summary before any link. Her colleague asks ChatGPT the same thing on his phone. Her father asks in Hindi. Three answers, three sets of cited pages, and your brand is in one of them. That is the practical question for a bank, insurer, fund house or fintech in India: which of these AI engines deserve your team’s time first.

Which AI engines should an Indian financial brand track?

Track the engines your customers already open, and learn how each one finds pages. We have not found a reliable public figure for each engine’s share of financial questions in India, so rank them by reach and by how directly they cite sources.

This page expands one answer from our GEO and AEO FAQ for financial brands, where the short version lives.

Does Google still matter more than ChatGPT for finance searches in India?

Yes, for most financial brands. Google says there are no extra requirements to appear in AI Overviews or AI Mode beyond being indexed and snippet-eligible. It also says both features may run several related searches across subtopics to build one answer, a technique it calls query fan-out. For a lender, that means a question about a personal loan can pull in pages on eligibility, fees and repayment at once. A brand with one thin product page loses to a brand that answers each of those sub-questions on its own page.

ChatGPT is the second priority. OpenAI says a site that blocks OAI-SearchBot will not be shown in ChatGPT search answers, though it can still appear as a navigational link. GPTBot is a separate crawler used for model training. A security team that blocks every AI crawler by default removes the brand from ChatGPT’s cited answers without meaning to.

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What is different about AI answers for a regulated financial brand?

An engine quotes a paragraph, not a page. The disclaimer in your footer does not travel with the sentence the engine lifts. Indian financial regulators set disclosure rules that were written for ads and documents, and those rules still shape how a page should be built if you want it quoted safely.

• Mutual funds. SEBI requires the mutual fund standard warning in advertisements, and in audio-visual ads it must be displayed and voiced for five seconds. Our design decision for fund pages: every answer block that mentions returns carries the standard warning in the same paragraph, in plain text, so the risk line is inside anything an engine quotes.
• Insurance. IRDAI’s current policyholder protection regulations require an insurer’s advertisement to show its registered name along with its trade name or logo prominently, and ads issued through a distribution channel need the insurer’s written approval before they go out. Our design decision: the insurer’s registered name is written into the first sentence of each answer, not only in the logo, because an engine reads text and drops images.
• Loans. RBI’s Key Facts Statement rules require lenders to give borrowers a Key Facts Statement, including the annual percentage rate, before the loan contract is executed. Our design decision: loan answer pages never quote a rate the Key Facts Statement could contradict. They explain what the annual percentage rate includes and where the borrower sees it before signing.

An AI engine quotes your paragraph, not your footer, so the disclosure has to live inside the paragraph.

How should a financial brand decide where to start?

Start with the engine that is already sending questions your way, then fix the foundation that serves all five. A practical order for most Indian BFSI brands:

1. Check crawler access. Confirm robots.txt allows Googlebot, OAI-SearchBot and PerplexityBot, and that Bing can index the site. Decide separately whether to allow training crawlers such as GPTBot.
2. Write the answer pages. One page per real customer question, with a two-to-four-sentence answer at the top and the disclosure inside it.
3. Cover English and Hindi. AI Mode answers in Hindi, so a Hindi version of your top product questions is worth testing.
4. Track a fixed prompt set. Ask each engine the same 20 to 50 buyer questions every week and log who gets named and cited. We publish our own tracking in the Yamm Labs AI visibility log.

Our BFSI work has included TATA AIA Life Insurance, HDFC Life, Axis Bank, Paytm Money and Aditya Birla Sun Life Mutual Fund. We work from Gurugram and serve financial brands in India, the Gulf and elsewhere remotely. The full service is on our AI visibility agency page for BFSI brands.

Worked example: one loan question across five AI engines

Take a single customer question to a lender: “Am I eligible for a personal loan on my salary, and what will it cost?” Here is how the same question plays out across the AI engines in the table above, using only what each one says about how it finds pages.

In Google AI Mode, the question may be split into related searches on eligibility, fees and repayment before one answer is written. A lender with a single product page offers one passage for three sub-questions. A lender with a separate, plainly written page for each sub-question offers three passages, each of which can be cited.

In ChatGPT search, the lender can only be cited if OAI-SearchBot is allowed to crawl the site. If the security team blocked every AI crawler last year, the page simply is not a candidate, however good it is.

In Perplexity, a quoted page gets a visible link, so the answer block needs to make sense on its own when a reader clicks through. In Copilot, the answer is grounded in Bing results, so a page that Bing has not indexed cannot be used. In Gemini, a related source may be shown alongside the response.

The page that works across all five AI engines looks the same: indexed, crawlable by search bots, one question per page, a short answer at the top, and an explanation of what the annual percentage rate includes and where the borrower sees it in the Key Facts Statement before signing. It never states a rate that the Key Facts Statement could later contradict.

Common mistakes financial brands make with AI engines

Most of these problems are not about content quality. They are about access, structure and disclosure. These are the mistakes worth checking first.

• Blocking search crawlers along with training crawlers. A single robots.txt rule that blocks all AI bots also removes the brand from ChatGPT’s cited answers. Search access and training access are two separate decisions and should be recorded separately.
• Leaving the disclaimer in the footer. AI engines lift a paragraph. A mutual fund standard warning that lives only in the footer, or inside an image, does not travel with the quoted text.
• Writing the insurer’s name only in the logo. Engines read text and drop images. If the registered name is not in the words of the answer, it is missing from the quote.
• One long product page for every question. Query fan-out rewards brands that answer eligibility, fees, documents and repayment on their own pages. One page that covers everything thinly competes poorly with a set of focused pages.
• Tracking only Google. Google comes first for reach, but a brand that never checks ChatGPT, Perplexity, Gemini or Copilot does not know where it is missing.
• Treating Hindi as a later project. AI Mode answers in Hindi. If your top product questions have no Hindi version, another brand’s page may be the one quoted.

A step-by-step AI engines checklist for BFSI teams

Use this as a working list in one review meeting with marketing, compliance and the web team in the room. Each step has one owner and one output.

1. Crawler policy (web or security team). List Googlebot, Bingbot, OAI-SearchBot and PerplexityBot, and record whether each is allowed. List training crawlers such as GPTBot separately, with the reason for the decision.
2. Index check (web team). Confirm the product and help pages you want quoted are indexed in Google and Bing and are eligible to show a snippet.
3. Question inventory (marketing). Collect the real questions customers ask, from call centre logs, branch staff and search queries. Group them by product.
4. Answer pages (marketing, with compliance). Write one page per question, with a two-to-four-sentence answer at the top and the required disclosure in the same paragraph.
5. Disclosure review (compliance). Review each answer block on its own, as an engine would quote it. Confirm the current text of the relevant SEBI, IRDAI or RBI requirement with your compliance team before approval.
6. Language pass (marketing). Pick the product questions that matter most and publish a Hindi version.
7. Prompt tracking (marketing or agency). Run the same set of buyer questions in each engine every week and log which brands are named and which pages are cited.

How to brief an agency or team on AI engines

A good brief is short and specific. It tells the team which engines to track, which questions matter, and what compliance will and will not approve. Include these points.

• The engines in scope. Name them in order of priority. For most Indian financial brands that is Google AI Overviews and AI Mode, then ChatGPT search, then Perplexity, Gemini and Copilot.
• The questions. Give the team your top buyer questions per product, in the words customers use, in English and Hindi.
• The disclosure rules. Share the approved wording for the mutual fund standard warning, the insurer’s registered name, or the Key Facts Statement explanation, and state that each must appear inside the answer paragraph.
• The crawler decision. Tell the team which crawlers are allowed today, and who can change that.
• The measure. Agree on a fixed prompt set and a weekly log of mentions and citations, rather than a single ranking number.

An agency that cannot explain how each engine finds pages, or that suggests hiding disclosures to make answers shorter, is the wrong fit for a regulated brand.

People also ask

Should an Indian bank block AI crawlers?

Block training crawlers if your policy requires it, but think twice before blocking search crawlers. OpenAI says sites that block OAI-SearchBot will not be shown in ChatGPT search answers. Treat search crawlers and training crawlers as two separate decisions.

Do AI Overviews need special markup to cite a financial page?

No. Google says there are no additional requirements to appear in AI Overviews or AI Mode beyond being indexed and eligible for a snippet. Clear answers, clean structure and accurate structured data that matches the visible text still help.

Can an AI engine show a mutual fund answer without the SEBI warning?

Yes, if the warning sits in a footer or an image. Engines lift text blocks, so the fund house cannot control what gets quoted. Putting the standard warning in the same paragraph as any return figure is the safest way to keep it attached.

Which AI engines matter most for an Indian insurer?

The same order applies: Google AI Overviews and AI Mode first, then ChatGPT search, Perplexity, Gemini and Copilot. What changes for an insurer is the page itself. Write the insurer’s registered name into the first sentence of each answer, so it survives when an engine quotes the text and drops the logo. Confirm the current IRDAI advertisement requirements with your compliance team before publishing.

Is ChatGPT or Perplexity better for a fund house to focus on?

Neither needs a separate content plan. Both read the open web through their own crawlers, OAI-SearchBot and PerplexityBot, so the same crawlable answer pages serve both. ChatGPT search suits research-style questions such as comparing funds. Perplexity shows citations prominently, so a quoted page gets a visible link. Check that both crawlers are allowed, then track both with the same prompt set.

How often should a financial brand check AI engine answers?

Weekly is a practical rhythm. Ask each engine the same fixed set of buyer questions and log which brands are named and which pages are cited. A fixed set matters more than a large one, because it lets you compare week to week. Change the set only when your products or your customers’ questions change.

Does Microsoft Copilot matter for B2B financial services?

It can. Copilot is built into Microsoft’s workplace tools, where finance and treasury teams research vendors, and it grounds answers by sending generated search queries to Bing. A B2B lender, payments company or treasury platform should confirm its pages are indexed in Bing, not only in Google, so Copilot has something to ground on.