NeuGenM.AI recently published the first AI (GEO/AEO) Performance Audit of India’s Top 100 brands and launched The NeuGenM Signal, a suite built to make brands visible in AI-driven discovery. The audit found that while 99.2% of these brands are cited when searched by exact name, only 12.4% surface when consumers ask AI engines for a recommendation within their category — and not one brand scored higher than a ‘C’ grade.
The finding points to a structural blind spot. Legacy brand equity still protects India’s leaders in direct, branded searches. But in the category-led discovery that increasingly happens inside tools like ChatGPT, Gemini, and Claude — where consumers ask for the best option rather than a brand by name — those same leaders are largely absent, ceding ground to nimbler, AI-optimized competitors at the exact moment purchase intent peaks.
Medianews4u.com caught up with Beerajaah Sswain, Managing Partner and Chief Digital & E-commerce Officer at NeuGenM.AI
Q. How do you test if India’s biggest brands are visible on AI tools like ChatGPT?
We built a proprietary methodology around our India AI Readiness Score™ — a five-pillar framework that measures how well a brand shows up when consumers ask AI engines for advice, recommendations, or answers.
Here’s what we actually do: we take a brand — say, a major FMCG player or a leading bank — and we run over 200 carefully designed prompts across five AI platforms: ChatGPT, Gemini, Perplexity, Claude, and Google AI Mode. These prompts mirror how real Indian consumers talk to AI — things like “best cement for home construction in India,” “which health insurance should I buy,” or “suggest a good shampoo for dry hair.” We run them in English and in vernacular languages like Hindi, Tamil, Bengali, and Marathi.
Then we score each brand on five pillars: GEO Presence (does the brand get mentioned at all in AI-generated answers?), Content Readiness (is the brand’s website structured in a way that LLMs can actually read and digest?), AI Share of Voice (when the brand is mentioned, how prominent is it compared to competitors?), Vernacular Coverage (does the brand show up in non-English AI responses?), and Sentiment Alignment (when AI talks about the brand, is it accurate and positive, or is it outdated and misleading?).
This gives us a composite GEO Pulse Score — a single number, graded A through F, that tells you exactly where you stand in the AI answer economy.
For our India AI Readiness Report, we did this across 100 of India’s biggest brands spanning eight industry verticals. That’s over 20,000 data points, verified by our Signal team.
Q. Your recent report gave every top Indian brand a C grade or lower. Did any specific sector perform worse than the others?
Yes, and the variation tells an important story.
BFSI — banking, financial services, and insurance — was the best-performing vertical, and even they only managed a C+. The reason is structural: financial services companies are forced by regulation to publish detailed, structured content — product disclosure documents, rate tables, compliance pages. That structured data happens to be exactly what LLMs find easy to ingest, so these brands got an accidental head start.
At the other end, legacy FMCG brands performed significantly worse. D2C brands — the digitally-native direct-to-consumer players — outperformed legacy FMCG giants by a factor of 2.4 times on AI readiness. This isn’t because legacy FMCG spends less on marketing — they spend orders of magnitude more. It’s because D2C brands were born with content architectures that LLMs can digest: structured product pages, ingredient lists, comparison content, user-generated review ecosystems. Legacy FMCG brands built their digital presence around display ads and campaign microsites — content formats that are essentially invisible to AI engines.
Telecom, Automotive, Pharma, and Retail all clustered in the C-minus to D range. The universal pattern we saw is that branded queries (asking about a specific brand by name) returned reasonable results — about 99.2% of the time, AI engines could talk about a brand when asked directly. But non-branded category queries — the kind where a consumer is asking for recommendations without naming a brand — saw AI recommendation presence drop to just 12.4%. That gap is the crisis. When a consumer asks “which brand should I buy,” Indian brands are largely absent from the answer.
Q. Since big brands are failing your AI visibility tests, what is the very first step they must take to fix this problem?
The first step is not technology. It’s a content architecture audit.
Most Indian brands have websites and digital assets built for the Google search era — they’re designed to rank on traditional search results pages through keywords, backlinks, and meta tags. AI engines don’t work that way. They synthesise answers from structured data, schema markup, entity relationships, and authoritative third-party sources.
So the very first thing a brand must do is answer one question: “When an AI engine reads my entire digital footprint — my website, my product pages, my FAQ section, my third-party presence on review sites and comparison platforms — can it actually understand what I sell, who I serve, and why I’m better than my competitors?”
In almost every audit we’ve run, the answer is no. The brand’s owned website contributes zero per cent of the citations that appear in non-branded AI-generated category answers. The citations are coming from review portals, editorial platforms, forums, and listing sites — sources the brand doesn’t control.
The fix starts with making your owned content LLM-digestible. That means implementing proper schema markup, building structured FAQ and comparison content on your own domain, publishing authoritative long-form content that directly answers the category questions consumers are asking AI engines, and ensuring your content is available in vernacular languages. This is not a six-month project — our NCS2M™ methodology is designed to deliver the first measurable improvements within the first 48 hours of deployment for schema-level fixes, with full content architecture overhauls completing within 8 to 12 weeks.
Q. Why would an established brand trust your Bangalore consulting firm over global agency networks that they have worked with for decades?
Because global agency networks are optimised for a world that is ending.
The holding-company agency model was built to buy media at scale — TV, print, outdoor, and later, programmatic display and paid search. That model works when the consumer journey starts with a Google search and ends with a click. But when a consumer asks ChatGPT “which cement should I use for my house” or tells Gemini “find me a health insurance plan for my family,” the traditional media plan is irrelevant. No amount of display ad spend or SEO keyword optimisation will get you into that AI-generated answer.
Our founders — Amrit Thomas and Ashish Thukral — are C-suite veterans who spent decades inside those global organisations. Amrit’s career spans multinational leadership in marketing transformation; Ashish has nearly three decades across banking, beverages, media, content, and sports at the highest levels. They know how those networks operate from the inside. They also know exactly where those networks have a structural blind spot: none of them have built proprietary AI visibility products, because their revenue models depend on media buying commissions that AI engines don’t generate.
What NeuGenM brings is a purpose-built capability that doesn’t exist inside any global agency network. Our Signal product is a GEO (Generative Engine Optimisation) platform designed specifically for the Indian and South Asian market. We’ve audited brands across India, Vietnam, Malaysia, and Singapore.
We have a proprietary AI engine — Cortex.ai — powering our paid media optimisation. We have a unified data platform — NeuGenM Sigma — that integrates over 300 data source connectors with identity resolution built for India’s DPDP Act compliance requirements. And we operate with the speed and decisiveness of a founder-led firm, not a holding company committee.
The question isn’t whether a brand should leave their global agency. The question is whether their global agency can solve this specific problem. If the answer is no — and right now, it is — then NeuGenM is the specialist partner that fills that gap.
Q. Smaller, tech-savvy startups are using AI to steal market share from legacy companies. How can old-school Indian businesses fight back before they lose their customers?
This is exactly the dynamic our report quantified. D2C brands are outperforming legacy FMCG giants by 2.4 times on AI readiness — not because they have bigger budgets, but because their content architecture was born AI-ready.
Legacy companies have three structural advantages that no startup can replicate: brand trust built over decades, distribution networks that reach every pin code in India, and marketing budgets that can move markets overnight. What they lack is the connective tissue between those assets and the AI answer layer.
The fight-back strategy has three parts.
First, own the category narrative in AI engines. When a consumer asks an AI tool “best paint for Indian climate” or “most reliable car for Indian roads,” the brand that has published the most authoritative, structured, citation-worthy content on that topic wins the recommendation. Legacy companies have decades of expertise — they just haven’t packaged it in a format that AI engines can consume. That packaging is what NeuGenM Signal does.
Second, activate your existing data advantage. Large Indian companies sit on massive first-party data assets — CRM databases, loyalty programme data, dealer and distributor networks, customer service records. Startups don’t have this. Our Sigma platform connects those data sources, resolves identity across touchpoints, and activates that data for AI-era targeting — including within LLM conversation environments through our Signal Ads product.
Third, move at startup speed on AI adoption. This is the hardest part. Legacy companies must adopt what we call the “Think Big, Act Small, Scale Fast” philosophy. Don’t run a twelve-month digital transformation programme. Pick one product category, run a GEO audit in a week, deploy content fixes in 48 hours, and measure the Pulse Score improvement in 30 days. Then scale what works. That’s the NeuGenM Springboard methodology — diagnose, immerse, consult, execute — compressed into sprints, not annual plans.
Q. You talk a lot about consumer neuroscience. Can you explain, without using scientific jargon, how tracking brain responses helps a brand sell more products?
Here’s the simplest way to think about it.
When you watch an ad, read a product description, or scroll past a brand’s post on Instagram, your conscious mind might say “that’s nice” or “I don’t care.” But underneath that, your brain is having a much more honest reaction — your attention spikes or wanders, your emotional engagement rises or flatlines, and your memory encoding either locks in or doesn’t. These reactions happen in milliseconds, before you can rationalise or filter them.
Consumer neuroscience uses tools that measure those real reactions — things like eye-tracking (where exactly are you looking on the screen, and for how long?), facial expression analysis (did your face show surprise, delight, confusion, or nothing at all?), and biometric signals (did your heart rate or skin response change when the brand message appeared?).
Why does this matter for marketing? Because traditional research asks people what they think, and people are terrible at answering that question honestly. They say they loved an ad but can’t remember it the next day. They say they’d buy a product but never do. They say price matters most but actually choose based on packaging colour.
What consumer neuroscience gives a brand is the truth underneath the stated preference. It tells you which three seconds of your thirty-second ad are actually creating memory. It tells you which product image on your e-commerce page is getting looked at and which is being skipped entirely. It tells you whether your packaging redesign is triggering positive emotion or confusion.
At NeuGenM, this informs everything from creative strategy to content architecture. When we build content that’s designed to be cited by AI engines, we’re not just optimising for algorithms — we’re building on an understanding of how human attention and memory actually work, so that when a consumer does encounter the brand (whether through an AI recommendation or a physical shelf), the message lands.
Q. When your team builds an AI system for a client, do you use existing software or do you write the code from scratch?
Both, and the split is deliberate.
Where proven, enterprise-grade infrastructure exists, we use it and brand it under our own product layer. Our data platform, NeuGenM Sigma, is built on top of enterprise-grade data infrastructure with over 300 source connectors, identity resolution capabilities, and activation orchestration — we’ve customised and whitelabelled this for the Indian market with DPDP Act compliance, local data residency, and integration with the platforms Indian brands actually use. There’s no reason to rebuild a data pipeline from scratch when battle-tested infrastructure exists.
But where no existing tool solves the problem, we build from zero. Our Cortex.ai engine — which powers NeuGenM Surge, our paid media optimisation product — is proprietary AI that we’ve built in-house. The GEO Pulse scoring methodology, the India AI Readiness Score™ framework, the NCS2M™ content optimisation methodology, and our Signal Ads product (which places contextual advertisements inside LLM conversations via the our LLM Open Exchange network) — all of these are original IP built by our engineering and strategy teams.
Our CTO, Gautam Arora, leads the technology architecture. The philosophy is simple: never rebuild what already works well, but never outsource the intelligence that creates competitive advantage. The scoring engine, the audit framework, the AI-native ad placement technology — those are our moat, and they’re built in-house.
Q. Mandeep Malhotra joined to handle experiential marketing. How do physical, real-world events connect with software that optimises brands for AI search?
Mandeep brings nearly three decades of expertise in experiential marketing, brand activation, and media strategy through his practice, TONIC. His role at NeuGenM connects to a principle most pure-play digital agencies miss entirely: AI engines don’t just ingest web content. They ingest the digital footprint that real-world events generate.
When a brand activates a physical experience — a product launch, a pop-up, a sponsored event, a stadium activation — that event generates content across dozens of channels: social media posts, news coverage, blog reviews, YouTube videos, Instagram stories, influencer mentions, and user-generated content. That content becomes part of the brand’s citation ecosystem — the pool of third-party signals that AI engines use to decide whether and how to recommend the brand.
The problem most brands have is that their experiential marketing and their digital content strategy operate in completely separate silos. The events team runs the activation, the PR team handles press, the social team posts content, but nobody is designing the event to generate the specific types of structured, citable, AI-digestible content that would improve the brand’s GEO Pulse Score.
That’s the connection Mandeep’s practice creates. When we design an experiential activation through NeuGenM, we’re simultaneously engineering the content outputs — the structured data, the entity-rich media coverage, the third-party endorsements — that feed back into the brand’s AI visibility layer.
The physical event becomes a content engine for AI discoverability, and the AI visibility data informs where and how to design the next physical activation.
Q. How can physical billboard campaigns or real-world events help an old-school Indian brand show up higher in AI search engine recommendations?
This is a question we hear constantly from traditional marketers, and the answer surprises them: offline media already influences AI recommendations — most brands just don’t know how to engineer that connection.
AI engines like ChatGPT and Gemini build their understanding of brands from the entire internet — not just the brand’s own website. When a billboard campaign goes live in Mumbai and people tweet about it, photograph it, write blog posts mentioning it, or journalists cover it as a creative marketing story — all of that content becomes part of the citation pool that AI engines draw from.
The problem is that most billboard and OOH campaigns are designed for eyeball impressions and then they end. Nobody is thinking about what happens to the digital ripple that the physical campaign creates.
Here’s how an old-school brand can engineer this connection. First, design offline campaigns that are inherently shareable and searchable — a billboard that provokes social media conversation generates content that AI engines can cite. Second, ensure that every offline activation has a structured digital shadow: a dedicated landing page with schema markup, a press release with entity-rich content, social media content with proper hashtags and brand mentions.
Third, track the AI visibility impact — run a GEO Pulse audit before and after the campaign to measure whether the offline activity actually moved the brand’s AI recommendation score.
We’ve seen this pattern in our audits: brands with strong editorial and PR ecosystems — brands that generate a lot of third-party media coverage — tend to have higher non-branded AI recommendation scores.
The content doesn’t have to be digital-first to matter. It just has to end up in the digital ecosystem in a structured, citable format.
Q. Some critics say that AI recommendations are biased and unreliable. How do you make sure that your tools are giving companies accurate marketing data?
This is a legitimate concern, and we take it seriously precisely because our business depends on the accuracy of the data we deliver.
First, the methodology: when we run a GEO audit, we’re not asking AI engines a single question and taking the first answer at face value. We run over 200 prompts per brand across five different AI platforms. We test the same question in multiple formulations. We run branded queries (where we mention the brand by name) and non-branded category queries (where we ask for generic recommendations).
We test in English and in vernacular languages. Each response is captured, categorised, and scored against a consistent framework. This multi-platform, multi-prompt, multi-language approach is designed to wash out the biases that any single AI engine might have.
Second, we separate what AI engines say from what we recommend. Our GEO Pulse Score tells you where you stand — it’s a measurement tool, not an oracle. When we tell a brand “your non-branded Pulse Score is 18 out of 100 and your competitor’s is 42,” that’s an empirical observation about what AI engines are currently recommending to consumers. Whether those AI recommendations are “fair” is beside the point — what matters is that consumers are receiving them, and they’re making purchase decisions based on them.
Third, we verify against real-world signals. Our Sigma data platform connects AI visibility data with actual business outcomes — conversion rates, CPA trends, revenue attribution. Our research found that brands with strong GEO signals show lower paid media cost per acquisition. That correlation between AI visibility and paid performance validates that we’re measuring something real, not a vanity metric.
The critics are right that AI recommendations can be biased. Our job is to measure that bias accurately, help brands understand where it helps or hurts them, and build content strategies that ensure the AI’s representation of the brand is accurate, complete, and competitive.
Q. What is the biggest mistake that Indian marketing executives make when they try to deploy AI tools in their daily office workflows?
They treat AI as a productivity tool instead of a strategic capability.
The pattern we see over and over: a CMO or marketing head gets excited about AI, asks the team to start using ChatGPT or Copilot for writing emails and generating social media captions, and then declares that the organisation is “AI-enabled.” Three months later, nothing meaningful has changed — the same campaigns are running, the same metrics are being tracked, and the AI tools have been reduced to a slightly faster way of doing the same work.
The mistake is starting with “how can AI make our existing processes faster” instead of “what can AI tell us about our market that we couldn’t see before?”
The CMO who asks “can AI write my ad copy faster” is asking the wrong question. The CMO who asks “are consumers asking AI engines to recommend my competitor instead of me, and if so, why?” is asking the right one. One question optimises the existing playbook. The other reveals an entirely new competitive battlefield.
The second most common mistake is siloed adoption. The digital team experiments with AI tools, but the brand team, the media team, the sales team, and the CXO are not in the room. AI visibility is not a digital marketing problem — it’s a brand strategy problem that touches content, product, PR, customer experience, and data infrastructure simultaneously. When it’s treated as a digital team project, it stays small and fragmented.
Our Springboard methodology exists specifically to avoid this mistake. It starts with a cross-functional discovery process — mapping stakeholders across six functional areas, diagnosing where AI readiness gaps exist across the entire organisation, not just the marketing team — before any technology is deployed.
Q. Many AI startups burn through funding and shut down quickly. What is your revenue model, and how will you stay profitable over the next three years?
We’re not a venture-funded startup burning cash to chase growth. NeuGenM is a founder-led, built on a diversified model with four distinct income streams.
First, consulting and advisory. This is the foundation — GEO audits, AI readiness assessments, brand strategy, and CXO advisory engagements. These are project-based and retainer-based contracts with mid-to-large enterprises. Every audit we run for brands across India, Vietnam and Southeast Asia — generates immediate consulting revenue. This stream is profitable on its own.
Second, managed services. Through our sub-brands — Shelf for retail media, Swarm for social and influencer, Surge for paid search and social — we operate as an always-on agency partner, managing campaign execution on retainer. This creates recurring monthly revenue with high client retention.
Third, product and platform licensing. Signal Ads, our LLM in-conversation advertising product operating through our LLM Open Exchange network, generates media revenue from brands placing contextual ads inside AI conversations. As LLM usage grows — and it’s growing exponentially across India and Southeast Asia — this revenue stream scales with the market. Sigma, our data platform, generates platform licensing fees from enterprise clients.
Fourth, training. Our Marketing.AI Academy delivers AI training and coaching programmes for marketing teams and business professionals. Indian enterprises are spending heavily on upskilling — this stream has low delivery cost and high margin.
The model works because no single stream has to carry the business. Consulting funds operations today; managed services create recurring revenue; products scale with the market; and training generates margin while deepening client relationships. We’re not dependent on raising the next round to survive the next quarter.
Q. The company runs a training academy for clients. Are Indian marketers eager to learn these new tools, or are they afraid of losing their jobs?
Both, and in that order.
The fear exists — we’d be dishonest to pretend otherwise. When a mid-career marketing manager sees an AI tool generate a media plan in thirty seconds that would have taken their team two days, the instinctive reaction is anxiety. When a content writer watches ChatGPT produce ten headline variations in the time it takes them to write one, they wonder about their relevance.
But what we’ve observed through our Marketing.AI Academy — and through every client workshop and training engagement we’ve delivered — is that the fear is quickly overtaken by something more powerful: the realisation that AI makes good marketers dramatically more effective, and that the marketers who learn these tools first will be the ones who lead, not the ones who get replaced.
The Indian marketer who understands how to run a GEO audit, interpret a Pulse Score, build LLM-optimised content, and use AI-powered tools for campaign optimisation is not competing with the AI. They’re competing with every other marketer who doesn’t know how to do those things. That’s a powerful career advantage, and the smart ones recognise it quickly.
One of our eight core beliefs at NeuGenM is that AI enhances human capabilities — it is not a competitor. That’s not a platitude. It’s an observable fact in every training session we run. The marketers who were the strongest strategists and creative thinkers before AI are the ones who become the most powerful users of AI tools. The tools amplify skill; they don’t replace it.
What we do see is a hunger for practical, applied training rather than theoretical overviews. Indian marketers don’t want a lecture on “what is generative AI.” They want to know: “Show me how to check if my brand is visible on ChatGPT, and show me how to fix it if it’s not.”
That’s exactly what our Academy delivers — hands-on, tool-specific, outcome-oriented training built around the same frameworks and methodologies we use with our enterprise clients.
The eagerness is real, and it’s growing faster than the fear.
