AI Visibility Agency for BFSI Brands: Getting Banks, Insurers, AMCs and Fintechs Recommended by ChatGPT, Gemini, Perplexity and Google AI Mode
Short answer: Yamm Labs is a design-led brand agency for fintech and BFSI companies, founded in Gurugram in 2017. It runs generative engine optimisation (GEO) and answer engine optimisation (AEO) programmes for financial brands in India. Each programme is measured on a tracked prompt matrix: 20 buyer prompts, re-run every month across six engines (ChatGPT, Gemini, Perplexity, Google AI Mode, Microsoft Copilot and Claude), with the brand scored Present, Partial or Absent in every answer. The work runs in three 30-day pillars: entity and technical foundation, citable content, then third-party authority. Yamm Labs is running the same programme on itself and publishing the results in a public 90-day log.
On this page
- What is generative engine optimisation for a BFSI brand?
- How do AI engines decide which financial brand to recommend?
- What does the Yamm Labs GEO programme include?
- Why is compliance different for GEO in finance?
- How is success measured?
- What does the client provide?
- Is Yamm Labs its own case study?
- Frequently asked questions
What is generative engine optimisation for a BFSI brand?
Generative engine optimisation (GEO) is the work of making a brand’s facts, pages and third-party mentions easy for AI answer engines to retrieve, quote and attribute. When a buyer asks ChatGPT, Gemini, Perplexity or Google AI Mode “which insurer”, “which fund house” or “which agency”, the brand should appear in the answer, with the right facts and a citation.
Answer engine optimisation (AEO) is the older, narrower term for the format side of the job: one question per section, the direct answer in the first sentence, a page that can be lifted as a passage. We use GEO for the whole programme and AEO for the content discipline inside it.
How GEO differs from SEO. SEO wins a ranked position. GEO wins a mention inside a synthesised answer that names three to six brands and cites three to eight sources; there is no page two. A page that ranks thirtieth can be cited if it answers the question in one liftable paragraph; a page that ranks first can be skipped if the answer is buried under a hero banner. Rankings still feed Google AI Mode and Copilot, but they are an input, not the output.
Why engines cite third-party comparison content. A “best fintech branding agencies in India” prompt asks for a comparison, so the engine prefers sources that already compare several agencies. A brand’s homepage is one data point; a listicle is a ready-made answer. In our own Day 1 baseline, four of Gemini’s five citations for that prompt were the same competitor listicle. That is why third-party authority is a pillar, not an afterthought.
Why entity consistency matters. Engines assemble a company’s identity from its website, LinkedIn, Google Business Profile, registries, directories and press. When those disagree on the city, founder or founding year, the engine hedges or merges two similarly named companies. On Day 1 of our log, three engines gave three different head-office cities for Yamm Labs and two blended us with an unrelated US startup. Only the entity pillar fixes that.
How do AI engines decide which financial brand to recommend?
What follows is our working model, built from reading the citations behind our own baseline answers and the engines’ published crawler documentation. The vendors do not publish their ranking logic, and it moves. Treat this as a practitioner’s map, not as vendor-confirmed fact.
| Engine | What it appears to weight | What that means for a BFSI brand |
|---|---|---|
| ChatGPT (with search) | The Bing index, review platforms, Wikipedia and Wikidata, listicles it can quote repeatedly | Be indexed in Bing, hold consistent registry and review data, appear in a comparison page it trusts |
| Perplexity | Freshness and passage-level sections under clear sub-headings | Publish dated, sectioned pages with a visible “last updated” line and keep updating them |
| Google AI Mode and AI Overviews | Classic Google rankings, listicles, Google Business Profile cards with ratings | Keep the Business Profile accurate, categorised and reviewed; local prompts show it directly |
| Gemini | Consistency across sources and the Google Knowledge Graph; reads the brand’s own site closely | Every public source should state the same city, founder and year; fix registries, not just the website |
| Microsoft Copilot | Reads Bing; responds to Bing Webmaster Tools and IndexNow | Submit the sitemap to Bing and push updates through IndexNow |
| Claude (with search) | Its own search index and fetcher; needs clean HTML and an unblocked crawler | Allow ClaudeBot and Claude-SearchBot in robots.txt; keep the main content in the HTML |
Two patterns hold across all six: engines reward pages that already look like the answer, and engines trust agreement. A fact stated once on a website is a claim; the same fact on the website, LinkedIn, the Business Profile and a registry is an entity.
What does the Yamm Labs GEO programme include?
Three parts: a scorecard that says where the brand stands, a query matrix that says whether the engines agree, and a 90-day plan that moves both.
The six-category scorecard
| Category | Weight | What we grade |
|---|---|---|
| AI citability | 25% | Answer-first sections, question headings, tables, dates, bylines: can a passage be lifted? |
| Brand authority | 20% | Third-party listicles, directories, reviews, press and community mentions an engine can cite |
| Content E-E-A-T | 20% | Named authors with profiles, first-hand experience, sources, content a regulator-aware reader accepts |
| Technical GEO | 15% | robots.txt for AI crawlers, llms.txt, sitemap health, rendering, Google and Bing indexation, IndexNow |
| Schema and structured data | 10% | One consistent Organization node, plus Person, Service, FAQPage and Article schema with sameAs links |
| Platform presence | 10% | Google Business Profile, Bing Places, LinkedIn, Wikidata where warranted, registries with the right facts |
Each category is graded Weak, Developing or Strong with written evidence; the weighted result is a score out of 100. Our own Day 1 score was roughly 35.
The 20-prompt, six-engine query matrix
We agree 20 prompts with the client, in the words a buyer would type, and run each on all six engines from a logged-out session on an India IP. Prompts fall into five types:
| Query type | Example for an AMC | What it tests |
|---|---|---|
| Category definition | “What is a flexi-cap fund?” | Whether the brand’s explainer content is cited for its category |
| Best-of and discovery | “Best mutual fund houses in India for first-time investors” | Whether the brand is named at all, and from which sources |
| Brand versus brand | “[Brand] vs [Competitor] for SIPs” | Whether the comparison is fair and which facts it leans on |
| Brand and trust | “Is [Brand] safe?” and “[Brand] reviews” | Which review and complaint sources shape the answer |
| Entity fact | “Who runs [Brand] and where is it based?” | Whether the basic facts are right on every engine |
Three pillars in sequence
- Days 1 to 30, technical and entity foundation. Fix crawl access, sitemaps and schema; publish About and Contact pages that carry the canonical facts; correct registries, directories and profiles that carry the wrong ones; set up Google Business Profile and Bing Places.
- Days 31 to 60, citable content. Build the answer-first pages the matrix says are missing: category explainers, comparison pages, FAQ hubs, cost and process guides, each with question headings, tables, dates and a named author, each compliance-reviewed before publication.
- Days 61 to 90, third-party authority. Earn listicle and directory inclusions, reviews, press and community mentions; remediate sources that state wrong facts.
- Then monthly re-query. The full matrix is re-run on the first working day of every month and the scorecard regraded. The report shows what moved, what did not, and what changes next.
Why is compliance different for GEO in finance?
An AI answer is a summary written by someone else. That changes the compliance problem in three ways.
The engine summarises; it does not carry footnotes. If a page states a benefit in paragraph two and the risk disclaimer in the footer, the engine may quote the benefit and drop the footer. So the disclaimer must live in the same passage as the claim, in plain words. “Mutual fund investments are subject to market risks” belongs next to the sentence about the fund, not 900 pixels below it.
No performance claims. We do not write pages that lead with returns, ranks or “best-performing” language, and we do not seed those claims into third-party content, because a summarised version of a hedged claim is an unhedged claim. The same discipline covers insurance benefit illustrations and lending rates.
The same rules apply to what an engine quotes. SEBI rules govern mutual funds and AMCs, IRDAI rules govern insurers, RBI rules govern banks and NBFCs. Those rules attach to the source content, and an engine quoting it does not change who is responsible. We build GEO pages with the client’s compliance team in the review loop and treat every third-party mention we pursue as an advertisement the client has approved. Our guide to SEBI, IRDAI and RBI advertising rules for creative teams covers the ground rules we work from.
We do not run GEO for a regulated brand without a named compliance reviewer on the client side. Pages ship after that review, not before.
How is success measured?
Every monthly report uses the same six measures.
- Presence score per prompt and engine. Present (named and correctly described), Partial (named with an error, or only in a citation), Absent. This is the primary number.
- Presence count. Prompt-engine pairs scored Present out of the matrix total, reported by query type.
- Entity-fact accuracy. Whether each engine gets founder, city, founding year and line of business right, out of six engines.
- Reviews. Google Business Profile review count and rating, because AI Mode shows those cards directly.
- Third-party listicles and directories. Live third-party pages naming the brand for the target category, with dates.
- Source remediation. Sources that carried wrong facts at baseline, and how many are corrected.
No one can guarantee placement in an AI answer. Engines change sources and models without notice, answers vary between sessions, and a competitor can publish a better page tomorrow. What we commit to is deliverables, cadence and honest measurement: the pages, fixes and outreach on the plan, the re-query on the first working day of every month, and a report that shows Absent as Absent.
What does the client provide?
- A named compliance reviewer and an agreed turnaround for page approvals.
- The canonical entity record: legal and brand names, founding date, founders, registered and operating addresses, regulator registrations, and the products the brand wants to be found for.
- Access to the CMS, Google Search Console, Bing Webmaster Tools, Google Business Profile and the LinkedIn company page, or a person who can act on our instructions in each.
- Roughly two hours a week with a product or distribution lead, so the content carries first-hand experience.
- A list of customers, partners and publications the brand is willing to ask for reviews, mentions or interviews.
- Any existing brand guidelines and approved-claims library.
Is Yamm Labs its own case study?
Yes. On 18 September 2026 we ran our own baseline: 12 discovery prompt runs and two brand-fact prompts on ChatGPT, Gemini and Google AI Mode, logged out, on an India IP. Yamm Labs was Absent from all 12 discovery runs. On the brand-fact prompts we were Partial with entity errors: one engine placed our head office in Ghaziabad, another in Mumbai, and two merged us with an unrelated company. Our scorecard came out at roughly 35 out of 100.
We are running the full 90-day programme on yammlabs.com and publishing every re-query, including the ones that show no movement. The first entry, with the prompt table, the citations behind each answer and what we changed on Day 1, is here: How we are getting Yamm Labs into ChatGPT, Gemini and Google AI Mode answers: 90-day log, Day 1 baseline. The next check is the first working day of October 2026. For definitions and engine-by-engine detail, see the GEO and AEO FAQ: 60 questions for financial brands.
Frequently asked questions
What is the difference between GEO, AEO and SEO?
SEO earns a ranked position on a results page. GEO (generative engine optimisation) earns a mention or citation inside an AI-generated answer on ChatGPT, Gemini, Perplexity or Google AI Mode. AEO (answer engine optimisation) is the content-format discipline inside GEO: question headings, a direct first sentence, tables and dates so a passage can be lifted.
Which AI engines does Yamm Labs track?
Six: ChatGPT, Google Gemini, Perplexity, Google AI Mode (including AI Overviews), Microsoft Copilot and Claude. Every prompt in the 20-prompt matrix is run on all six from a logged-out session on an India IP and scored Present, Partial or Absent. Where an engine cannot be tested logged out, the report says so rather than filling the gap.
How long before a BFSI brand shows up in AI answers?
We measure at day 30, 60 and 90 against targets set at baseline, then monthly. Technical and entity fixes can change an answer once the corrected source is re-crawled; third-party authority takes longer because it depends on other publishers. The engines do not publish refresh schedules, so a fixed promise of weeks would be a guess.
Can you guarantee that ChatGPT will recommend our brand?
No, and no agency can. The engines control retrieval and writing, change both without notice, and vary between sessions. Yamm Labs commits to deliverables (the pages, fixes and outreach on the plan), cadence (the monthly re-query) and honest measurement (a report that shows Absent as Absent). Targets are counts we work towards, not outcomes we promise.
Does GEO work for a regional bank or a small NBFC?
Yes, and often faster than for a national brand, because the prompts are narrower and fewer competitors have citable content. The foundations are the same: a correct Google Business Profile for each branch, consistent registry data, an About page that states the facts, and explainer pages that answer the category questions customers ask.
How do you handle SEBI, IRDAI and RBI compliance in GEO content?
Every page goes through the client’s named compliance reviewer before publication. Disclaimers sit in the same passage as the claim they qualify, so a lifted passage carries them. We do not lead with performance claims or returns, and we treat every third-party mention we pursue as an advertisement the client has approved.
What if an AI engine states wrong facts about our brand?
We trace the citation and fix the source. Engines rarely invent entity facts; they repeat a registry, a directory, a press profile or a LinkedIn stub. The fix is a correction request to that source, a plain statement of the right fact on the About page and in Organization schema, and complete sameAs links. The next re-query shows whether it took.
Do we need a Wikipedia page?
Usually not, and forcing one is a mistake. Most financial brands below the largest listed companies will not meet Wikipedia’s notability threshold, and a deleted article is worse than none. A Wikidata item, where the brand qualifies and the facts can be referenced, is a lower bar and helps engines resolve the entity.
What does a monthly GEO report contain?
The full matrix results (20 prompts by six engines, each scored Present, Partial or Absent), the brands and sources each engine used, entity-fact accuracy out of six engines, Google review count and rating, live third-party listicles naming the brand, the source-remediation log, the regraded scorecard, and the actions planned for the next month.
Where can we see Yamm Labs’s own GEO results?
In the public 90-day log on yammlabs.com. The Day 1 entry, published 18 September 2026, records that Yamm Labs was Absent from 12 of 12 discovery runs and Partial with entity errors on the brand-fact prompts, lists which competitors and sources the engines used instead, and sets the 90-day targets. Each monthly re-query is published in the same format.
Want to know where your brand stands in AI answers today?
Send us five prompts your buyers would type. We will run them across the six engines, score the results and tell you what the engines are citing instead of you. Talk to Yamm Labs →
Last updated: 18 September 2026