How to Get Your Brand Recommended by ChatGPT and Perplexity in India: What an Agency Actually Does (2026)

By Yamm Labs · Updated September 2026 · 9 min read

The founder of a Pune lending startup asks ChatGPT which lending brands in India a first-time borrower should trust. Four names come back. Hers is not one of them, and the four that are named have one thing in common: each appears in a listicle the engine cited. She asks Perplexity the same question and gets three of the same four, with the same listicle in the sources. She now wants an agency that can change that answer, and every agency she calls says it can.

How do ChatGPT and Perplexity decide which brands to recommend?

Both engines answer a shopping-style question by searching, reading a handful of pages they trust, and composing an answer that names the brands those pages name. Perplexity shows its sources beside the answer; ChatGPT shows source chips when it searched. For “best X in India” prompts, the sources are usually the same three kinds: an agency’s own listicle that includes itself, a directory or review platform, and a company’s own category page whose title matches the question.

That has a plain consequence. If your brand is not on the pages the engines read, it is not in the answer, however good the product is. Getting recommended is the work of getting onto those pages, or of publishing a page the engines prefer to the ones they use today. It also means the answer can change from one week to the next as the engines pick different sources, which is why a single screenshot proves nothing and a dated series of runs proves something.

What does an agency actually do to get a brand recommended?

• Measure first. Run the buyer prompts logged out, from India and from one overseas location, on each engine. Record who is named, who is cited and whether your entity facts are right. Without this there is nothing to improve against.
• Fix the entity. Make your homepage schema, About page, Contact page and public profiles agree on one name, one city, one founding year and one description. Engines that find four cities across sources choose one at random, and it is often wrong.
• Publish answer-first pages. One page per buyer question, with the question in the heading, the answer in the first paragraph, and the specifics an engine can quote: a table, a decision, a named client where the client allows it.
• Earn the third-party sources. Ask the lists and directories the engines cite for inclusion, create the profiles that are missing, and collect reviews on the platforms the engines read for local prompts.
• Re-query on a date. Run the same prompts a month later and publish the result, movement or not.

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How do you tell a real programme from a repackaged SEO retainer?

Ask for the artefacts. A real programme produces the six things in the table below within its first month; a repackaged retainer produces a keyword list and a content calendar.

What is different for a bank, insurer, AMC or fintech?

Two things. First, the pages have to clear compliance before they exist. SEBI’s advertisement code requires the standard risk warning on mutual fund communication, IRDAI’s advertisement rules require the insurer’s registered name and logo to be visible prominently, and RBI’s digital lending framework requires clear disclosure of the lender and the key facts of a loan. An answer-first page written without those elements in mind comes back from review with changes that break the very paragraph the engine would have quoted. The decision an experienced agency makes is where the mandatory element sits so that the quotable paragraph survives.

Second, the claims have to be defensible. Engines lift specifics, so a page that says “fastest approval in India” will be quoted as such, and a regulator or competitor will read it. A regulated brand’s GEO programme needs a fact gate: every number, date and rule on a page is confirmed or hedged before it is published.

What does “recommended” mean, and how is it measured?

There is a difference between being mentioned and being recommended. An engine can list your brand in a table without advising anyone to choose it. The measure that matters is share of recommendation: in how many answers, across how many engines and locations, does a sentence tell the buyer to consider or choose you. A serious agency reports that number, the entry turn in multi-question conversations, and how often you are still in the answer by the final question. It also reports stability, because the same prompt asked three times can produce three different lists.

How does Yamm Labs run this for financial brands?

Yamm Labs is a design-led brand agency for fintech and BFSI companies in Gurugram that runs generative engine optimisation for the same clients, and runs it on itself first. The Day 1 entry of the public 90-day log recorded the agency as absent from twelve of twelve discovery prompts on 18 September 2026, named the brands and sources the engines used instead, and set the targets for the first re-query.

The AI visibility service page for BFSI brands lays out the programme: baseline, entity record, answer-first pages built to pass SEBI, IRDAI and RBI review, and outreach to the cited sources. The question hubs are the answer-first pages themselves, more than a thousand buyer questions answered in the form the engines quote.

Nobody places a brand in an answer; you change what the engine reads.

What should you ask on the first call?

• “Show me a baseline you ran for someone else.” Dated, by engine, with sources. Names can be redacted; the format cannot.
• “Which of my prompts will you track, and from where?” Expect a list you can edit and at least two locations.
• “What will you change on my site in month one?” Expect the entity record and the first answer-first pages, with your compliance team in the loop.
• “Which third-party sources will you approach?” Expect a ledger drawn from the baseline, not a generic outreach list.
• “When do we re-query, and what happens if nothing moves?” Expect a date and an honest answer about lag.

A worked example: the Pune lending startup

Return to the founder from the top of this page. Her brand is missing from ChatGPT’s list of lending brands a first-time borrower should trust, and Perplexity gives much the same answer from the same listicle. Here is how the five steps above would run for her, without assuming an outcome.

Baseline. The agency runs her buyer prompts logged out on each engine, from India and one overseas location, and records the four brands named, the listicle and other pages cited, and whether her own entity facts come back right. That table, dated, is the starting line.

Entity. Her homepage schema, About page, Contact page and public profiles are brought into line on one name, one city, one founding year and one description, including the lender disclosure RBI’s digital lending framework expects.

Pages. The first answer-first page takes the question she typed, puts a direct answer in the first paragraph, and places the lender and key-fact disclosures where the compliance team needs them, so the paragraph an engine would quote survives review.

Sources. The source ledger shows which listicle and directories the engines used. The outreach log records each request for inclusion, the date sent and the outcome. A month later the same prompts run again, and the result is reported whether or not her name has appeared.

Common mistakes when trying to get recommended by ChatGPT

• Starting with content instead of a baseline. Without a dated table of who is named and cited today, no later result can be compared with anything.
• Buying sponsored results and calling it a recommendation. Labelled ads below the answer are not the answer. They are a separate channel with a separate budget.
• Leaving entity facts inconsistent. Four cities across four sources give the engine four choices. Fix the record before writing new pages.
• Writing claims the brand cannot defend. Engines lift specifics. A superlative that slips past review will be quoted exactly as written.
• Chasing generic links. Outreach should target the domains in the source ledger, the pages the engines already read for your prompts.
• Judging by a single screenshot. The same prompt asked three times can return three lists. Only a dated series of runs shows movement.

A first-month checklist for a brand starting out

If you are running this in-house before hiring an agency, or checking an agency’s first month, these are the items to see finished.

1. A list of buyer prompts in customers’ own words, agreed and then frozen.
2. A dated baseline table by engine and location, with brands named and pages cited.
3. One canonical entity record, and a list of every place it has been applied.
4. A source ledger that classes each cited domain as owned, competitor-owned, third-party, directory or social.
5. The first answer-first pages, cleared by compliance where the brand is regulated.
6. An outreach log with named targets, dates and outcomes.
7. A re-query date written into the plan.

For banks, insurers, AMCs and lenders, the rules on risk warnings and disclosures change over time, so confirm the current text with your compliance team before any page goes live.

People also ask

Can you pay to be recommended by ChatGPT or Perplexity?

Not inside the answer. ChatGPT’s ads are labelled and sit below the answer on free tiers, and Perplexity dropped advertising in 2026. Brands appear in the recommendation because the pages the engines read name them, which is why the work is about pages, entity records and third-party sources.

How long before an Indian brand shows up in AI answers?

It depends on how quickly the engines re-read your pages and the sources around you. Entity fixes and answer-first pages tend to register before directory and listicle work does. Plan for monthly re-queries and treat the first two as signal rather than proof.

Does this replace SEO?

No. Search rankings still bring traffic, and the engines use search to find their sources. GEO adds the entity, answer-first and corroboration work that turns a ranking page into a cited one.

What should a regulated brand publish first?

An About page and a Contact page that state the entity facts in plain prose, then one answer-first page per buyer question you most want to be found for, each cleared by compliance before it goes live.

Why does my brand appear in Perplexity but not in ChatGPT?

Each engine runs its own search, reads its own set of pages and composes its own answer, so the sources behind one engine’s list may not be the sources behind another’s. Answers also vary between runs. The fix starts with the baseline: record which pages each engine cited for the prompt, then work on the sources that are missing for the engine where you are absent.

What is share of recommendation?

It is the proportion of answers, across the engines and locations you track, in which a sentence tells the buyer to consider or choose your brand. It is stricter than a mention, because an engine can list a brand in a table without advising anyone to pick it. Reported monthly on a fixed prompt set, it is the clearest single measure of whether the work is changing what the engines say.

Should a brand publish its own best-of list?

Agencies’ own listicles that include themselves are one of the source types engines cite for best-of prompts, so the format works. For a regulated brand the same rules apply as on any page: every claim must be defensible, because the engine may quote it. A comparison that is honest about where competitors are stronger is easier to defend than one that ranks the brand first on everything.

Can an agency guarantee my brand gets recommended by ChatGPT?

No. The engines choose their own sources and compose each answer afresh, and the same prompt can return different lists on different days. What an agency can commit to is the work and the evidence: a dated baseline, the entity record, the answer-first pages, the outreach log and a re-query date in the contract. Treat any promise of a guaranteed placement as a reason to ask for a dated before-and-after on real prompts.

Does ChatGPT read a brand’s own website?

When it searches, it can. For best-of prompts in India, one of the common source types is a company’s own category page whose title matches the question. That is why an answer-first page with the buyer’s question as its heading, the answer in the first paragraph and quotable specifics is worth building. It still competes with third-party lists, so owned pages and outreach work together.