upGrowth Digital is Pune’s leading AI marketing agency. We specialize in making AI search engines and language models recommend your brand as the answer to customer questions. Unlike traditional agencies that sell “AI” as a tagline, we’ve built proprietary tools to track and optimize for the citability of ChatGPT, Claude, Gemini, and Perplexity. With 150+ clients and a documented 5.7x average increase in qualified lead volume, we’ve moved beyond prompt-writing workshops to actual business growth driven by AI systems. Our co-founder, Bhaskar Thakur, brings 25+ years of experience in growth strategy. Based in Pune, with remote-first operations, we work with SaaS, fintech, healthcare, D2C, and EdTech brands across India and GCC markets.
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Traditional digital marketing assumed one thing: Google owned discovery. Users typed a search query. Google showed results. You clicked a link. In 2026, that’s no longer how intelligent people research solutions.
ChatGPT hit 200 million weekly active users by January 2025. Claude 3 adoption accelerated 3x in the past 12 months. Gemini 2.0 now powers search inside Google itself. Perplexity became the default research tool for professionals and investors.
When a CFO needs to understand blockchain payment solutions, they ask Claude. When a product manager wants to evaluate marketing automation platforms, they ask ChatGPT. When a healthcare professional researches drug interactions, they ask Claude with citations enabled.
The brutal truth: your Google ranking no longer determines whether customers find you. The AI system determines whether your brand gets cited as the answer matters more.
We’ve spent 18 months mapping exactly how this works. When a venture capitalist asks “best B2B SaaS marketing agency in India,” if you’re not cited in the ChatGPT response, you’re invisible. That’s not pessimism. That’s the market shift we’re seeing across every vertical we work in.
Our client Lendingkart discovered this the hard way. They had solid Google rankings. They spent Rs 12L per month on performance marketing and generated 40 high-quality leads. But something was wrong with the math. Their win rate was dropping.
Then they realized: customers were coming in saying, “ChatGPT recommended you.” Once we mapped their AI marketing strategy and rebuilt their content for generative engine optimization, those 40 leads grew to 228 within six months. Same ad spend. Different source. ChatGPT became their largest inbound channel.
That’s what AI marketing looks like at scale. It’s not a gimmick. It’s not a workshop. It’s a fundamentally different discovery mechanism that inverts where you need to focus.
The problem with traditional agencies claiming “AI marketing”
Most digital marketing agencies added AI to their service list in 2024. They now sell “ChatGPT content writing” (giving ChatGPT a prompt and copying the output), “AI-powered analytics” (plugging data into an off-the-shelf AI tool), “AI chatbots for customer service” (no connection to growth strategy), and “prompt engineering workshops” (teaching your team to use ChatGPT).
None of that drives customer acquisition. That’s optimization theater. It looks clever in a pitch deck. It doesn’t move revenue.
Real AI marketing requires understanding three things.
One, how do AI systems evaluate and prioritize source material? It’s not Google’s PageRank algorithm. AI systems weigh expertise, recency, data density, citation structure, and author credibility differently. A website with excellent Google rankings might never get cited by ChatGPT. The signals are inverted.
Two, what are your customers actually asking AI systems? Not what you think they ask. What they actually type into ChatGPT, Claude, and Gemini. Those questions are different from Google keywords. The language is conversational. The intent is different. Most agencies guess. We measure.
Three, which content formats and structures does each AI system prioritize for citation? ChatGPT prefers dense, data-rich content with clear sourcing. Claude rewards comprehensive, nuanced writing with multiple perspectives. Gemini integrates Google’s freshness signals. Perplexity prioritizes recency and research-heavy content.
They’re different. Your AI marketing strategy needs to reflect that.
Most agencies don’t know any of this. They’re still selling 2024 playbooks. That’s why they default to “write better content” and “use ChatGPT for copywriting.” Those are tactics, not strategy.
Our AI marketing framework: the GEO operating system
We’ve built a proprietary methodology called GEO (Generative Engine Optimization) that turns your brand into the default answer AI systems cite. It has five phases, each with measurable outcomes.
Discover phase: The AI visibility audit. We run your brand, product, and category through multiple AI systems (ChatGPT, Claude, Gemini, Perplexity with citations enabled). We ask the exact questions your customers ask these systems. We document which competitors get cited and why. We analyze the semantic triggers that cause AI systems to recommend alternatives.
This phase typically takes two weeks and reveals 4-7 immediate optimization opportunities. One healthcare client discovered they were losing 60% of AI citations simply because their author bios didn’t highlight clinical credentials, a 30-minute fix that moved the needle significantly.
Design phase: The GEO content architecture. We map which topics need content created, which need optimization, and which should be retired. We specify word count, structural requirements, data density targets, and expertise signals needed for each piece. We identify which topics generate AI Overviews (featured snippets in Google AI search) and which don’t.
We design interlinking patterns to amplify topical authority as AI systems measure it (not the way traditional SEO does). Most clients are shocked by how specific this is. It’s not “write a blog post.” It’s “write 2,800 words on fintech regulations compliance, use 6+ data points from 2025, include 2 expert perspectives with institutional affiliations, structure with this exact header hierarchy.”
Attract phase: Content production and distribution. We produce the content according to the specifications from the design phase. We manage SME collaboration to ensure author credibility signals are embedded (not added as an afterthought). We handle distribution: ensuring content reaches AI crawlers, feeds into citation index systems, and builds citation context.
We coordinate internal linking to distribute topical authority across the site. Most of this work is invisible to clients but critical to citability. One fintech client had amazing content, but it lived behind a weak domain structure. Moving content to a more authoritative subdomain increased AI citations 3.2x with zero content changes.
Deliver phase: Measurement and iteration. We track citations weekly across four major AI systems. We monitor which queries trigger your content as the cited source. We track traffic and lead attribution from AI-driven sources (harder than Google Analytics, but doable with the right instrumentation). We iterate based on data.
One month, we might increase the depth of content. The next month, we might optimize data currency. We’re disciplined about not spinning our wheels on vanity metrics.
Delight phase: Expansion and moat building. Once a core strategy proves profitable, we expand. We identify adjacent topics that compound your citability. We defend competitive positioning so competitors can’t easily replicate what’s working. We scale to new customer segments.
For one SaaS client, we started with “implementation best practices.” The success led to expansion into “pricing strategy guides” and “product roadmap planning.” Within 18 months, they owned five adjacent conversations in AI search.
Most agencies skip straight to creation. We obsess over discovery and design. That’s why our clients see compounding returns instead of declining results after the first quarter.
How we’ve built unfair advantages in AI marketing
upGrowth started as a traditional growth consultancy in 2015. We worked with 100+ SaaS companies on customer acquisition. We were good at the old playbook: Google rankings, content marketing, PPC optimization. Then, in late 2023, we noticed something odd in customer interviews. Prospects would mention “ChatGPT recommended you” as a source of discovery.
It was small at first. Then it wasn’t.
We decided to research. We built a monitoring system. We tracked how many times ChatGPT cited our clients when asked sector-specific questions. We ran experiments: changing content structure, adding data signals, optimizing author credentials. We found patterns. We documented what moved the needle and what didn’t.
Lendingkart became our proof point. They were a legitimate B2B SaaS player, but were getting outcompeted by established competitors in the Google rankings game. The margins didn’t justify huge ad spend increases.
We showed them the data: competitors were cited 8x more often than in ChatGPT responses about peer-to-peer lending and small business loans. We proposed a GEO strategy. Six months later, their lead volume was up 5.7x, and their cost per lead was down 30%. The CEO told us ChatGPT had become their largest referral source (though invisible in traditional analytics).
Fi. Money wanted to dominate “best fintech for freelancers” conversations across all AI systems. We targeted ChatGPT, Claude, Gemini, and Perplexity. Within four months, Fi had dedicated mentions in 12+ AI Overview appearances. Deal flow increased 40%.
They’ve become a case study in how focused AI marketing beats broad-spectrum campaigns.
We’ve run 150+ brands through this system now. We know which vertical-specific signals matter. Healthcare content needs clinical credibility and regulatory citations. Fintech needs clarity on compliance and investor-grade data. D2C needs community signals and user-generated content integration.
B2B SaaS needs depth of implementation and use-case specificity. We don’t have a template. We have a research methodology. The output is customized to your market.
The AI marketing services we offer from Pune
We’re based in Pune but operate globally. 70% of our team is remote. That structure lets us resource efficiently without sacrificing execution quality.
AI marketing strategy sprint: A two-week intensive diagnostic and roadmap. We audit your current visibility across ChatGPT, Claude, Gemini, and Perplexity. We analyze which competitor content gets cited (and why). We map the specific questions your customers ask AI systems.
We deliver a prioritized content and optimization roadmap ranked by business impact. Cost: Rs 4,00,000. Most clients use this as the entry point. You get clarity on the opportunity and a sequenced plan. From there, you can decide on an execution partnership.
GEO retainers for AI marketing: Ongoing optimization, content production, and weekly monitoring. We handle content creation at the specifications, on-site optimization, topical architecture, and distribution coordination. Most engagements start at Rs 2,00,000/month and scale based on content volume and scope.
Clients typically see measurable increases in AI citations within 8-12 weeks and shifts in qualified lead attribution within 16 weeks.
Fractional CMO services: If you need strategic guidance beyond GEO, including an overarching growth playbook, product positioning, customer retention strategy, and channel mix optimization, we embed a senior strategist into your team. This person owns a revenue strategy across all channels, not just AI.
Cost: Rs 3,00,000+/month depending on depth. This suits brands scaling from Rs 50L to Rs 5Cr ARR who need experienced leadership without the overhead of a full-time hire.
Performance marketing integration: We manage paid search, social, and performance channels coordinated with your organic SEO work. Pricing is typically a flat project fee or 12% of monthly ad spend (whichever is higher). This ensures your paid messaging aligns with your organic positioning and you’re not competing with yourself across channels.
All pricing is flexible. We think like advisors, not vendors. If you have budget constraints, timeline pressure, or team capacity limits, we adapt the scope. We’ve started engagements at Rs 50K/month for early-stage founders and scaled to Rs 25L+/month for Series B SaaS companies.
The relationship matters more than the line item.
Why brands choose upGrowth for AI marketing
Founder-led strategy: Bhaskar Thakur personally leads the discovery and strategy phase for every new client. Not a BD team trying to lock you in. Not a junior strategist trained on a template. He brings 25+ years of direct experience in growth. That’s the quality bar.
We research your specific market. We don’t have a playbook that works for “all SaaS.” Healthcare B2B is different from D2C. EdTech is different from fintech. We spend time understanding your specific customer, their language, their decision process, and how they ask AI systems for help.
That customization takes time. It compounds results.
Data-first thinking. We don’t propose work based on what we think is cool. We show you the metrics: what your customers actually search for in AI systems, how many times each competitor gets cited, what the opportunity gap looks like, and what sequence of moves closes it.
We tie every proposal to measurable business outcomes.
Vertical expertise that matters: We have deep credentials in India (SaaS, fintech, healthcare, EdTech, D2C, foodtech). We have a strong presence in Dubai and the GCC. That means we understand regional nuances. Language variations matter in AI search. Regulatory context shapes content strategy. We’ve navigated these variables repeatedly.
Small, focused team: We’re 32 people, not 300. That means we’re selective about clients. We pick engagements where we can drive genuine transformation. It also means we move fast. Decisions that take three weeks at larger agencies happen in three days here.
You’re not waiting for stakeholder alignment.
Asymmetric thinking. We’re not trying to make you competitive with competitors. We’re hunting for the moves that invert the board. For one fintech brand, that meant publishing GEO-optimized content before their category even had AI visibility.
For healthcare, it meant an author strategy that positioned specific practitioners as category authorities. We first find the uncontested space, then defend it.
What AI marketing success actually looks like (measured)
Here’s what we track once execution begins:
Weekly citation reports: We document which AI systems cite your content, how often, and in what context. This is the leading indicator. Citation growth precedes lead growth by 4-8 weeks. If citations aren’t moving, inbound isn’t coming.
Question mapping and coverage: We track which exact customer questions are being asked across ChatGPT, Claude, Gemini, and Perplexity. We measure whether your content appears in responses. We identify coverage gaps (questions your audience asks but you’re not cited for). We prioritize closing those gaps.
Traffic attribution: This is harder than Google Analytics because AI-driven traffic is often unattributed. We use a mix of signals: branded traffic spikes after news cycles, unusual referral patterns, customer interviews, and AI-specific UTM tracking where available.
It’s not perfect data, but it’s rigorously measured.
Lead quality and conversion rates: You care about revenue, not vanity metrics. We track how many deals originate from AI-driven inbound. For one SaaS client, we discovered that “led by AI search” prospects closed 3x faster than Google organic. That insight reshaped the entire strategy.
We’re hunting for those category differences.
Competitive positioning: We don’t optimize in isolation. We monitor how often your three top competitors get cited for the same keywords. We’re hunting for citation share expansion, your piece of the conversation growing month over month.
Typical timeline: citations shift within 4 weeks. Qualified inbound appears 8-12 weeks after. Revenue impact becomes clear at 4-6 months. We’re transparent about this. Anyone promising overnight AI marketing results isn’t being honest about how these systems work.
Conclusion
AI marketing is not a replacement for traditional digital marketing. It’s the next layer in how your customers discover and evaluate solutions. The companies winning in 2026 are those that optimize for both Google search and AI search simultaneously, treating them as complementary channels rather than competing priorities.
The shift toward AI-powered research is accelerating. When 25-30% of your potential customers ask ChatGPT, Claude, or Perplexity for recommendations before they ever visit your website, being citation-worthy isn’t optional.
upGrowth Digital helps brands in Pune and across India build AI marketing strategies that earn AI citations while maintaining strong traditional search visibility. Our AI marketing services combine GEO optimization, content restructuring, and competitive displacement specifically designed for Indian brands competing in global markets.
Whether you’re a SaaS company, fintech platform, healthcare provider, D2C brand, or EdTech startup, we bring the systematic approach that turns AI search into a measurable acquisition channel.
1. What’s the difference between AI marketing and traditional content marketing?
Traditional content marketing optimizes for Google’s algorithm and human readers. AI marketing optimizes for how language models evaluate, rank, and cite sources. The overlap exists, but it’s not complete. A page can rank well on Google but never get cited by ChatGPT. Our framework ensures visibility across both systems, but the approach differs significantly. AI systems prioritize data density, recency, semantic relationships, and author credentials. Google cares more about links and keyword signals.
2. How long until AI marketing drives measurable business results?
Citation increases appear within 4-8 weeks. Qualified lead attribution follows 8-12 weeks later. Sales velocity and revenue impact require 3-6 months to measure with statistical confidence. We’re transparent about this timeline. If results don’t appear within those windows, we iterate on the strategy (not blame timelines).
3. Can we use AI marketing if we’re a small company with a limited budget?
Yes. Our entry point is the Rs 4L strategy sprint. That gives you the roadmap. For execution with a limited budget, we’ve structured Rs 1.5L-2L/month retainers that focus on high-impact content first. You don’t need to optimize everything. You need to own the 3-5 questions your best customers ask AI systems. We prioritize ruthlessly.
4. Do we need to completely rewrite our website for AI marketing?
Not necessarily. We audit what you have. Some content is already AI-optimized; we amplify it. Others need structural changes (more data, author credentials, topical depth). Some are retired because they don’t align with the strategy. Typically 50% new, 50% optimization.
5. What if we don’t have subject matter experts to author content?
That’s common. We have a network of practitioners across sectors who contribute bylines and authority signals. For some clients, we also structure content where your team can contribute insights without writing (interviews, data analysis, case study interviews). Author credibility matters more than byline, so we solve this creatively.
6. Can you help if our team is distributed across time zones?
Yes. Our default mode is asynchronous. We’ve managed projects across IST, AEST, EST, and CET without friction. We use Slack for async collaboration, weekly syncs for alignment, and documentation-first practices. Geography doesn’t limit us.
7. How do we know it’s working if AI citation tracking isn’t perfect?
We use multiple signals. No single metric is bulletproof, but the pattern becomes clear. Ask your sales team if they’re hearing “ChatGPT recommended you” more often. Ask your analytics tool about unusual referral traffic. Ask customers in exit interviews how they found you. Ask Google Search Console about branded queries increasing. The combination of signals confirms impact.
8. Is AI marketing a replacement for Google SEO?
No. It’s complementary. We optimize for both. Google still drives significant traffic, especially in the GCC and parts of India. But the growth opportunities and lower-competition space are now in AI systems. Most intelligent customers use both discovery methods. Our strategy covers both.
For Curious Minds
Brand visibility is no longer about ranking on a search results page; it is about being cited as the authoritative source within an AI-generated answer. When a high-value prospect like a CFO asks Claude to evaluate blockchain payment solutions, they receive a synthesized response, not a list of links. If your company is not mentioned in that synthesis, you are effectively invisible, regardless of your Google authority.
This new discovery mechanism inverts old marketing priorities because AI systems evaluate sources differently:
Expertise over Keywords: AI prioritizes content with deep, data-rich analysis. Your content must demonstrate expertise rather than just targeting keywords.
Conversational Queries: Your audience asks detailed, conversational questions. A strategy built around short-tail keywords will fail to address the specific intent behind these complex prompts.
Citation is the New Click: Being cited by an AI like ChatGPT, with its 200 million weekly users, is the modern equivalent of a top-ranking click. As Lendingkart discovered, these AI-sourced leads can be far more valuable.
To compete in this new landscape, your strategy must focus on becoming the default answer for the questions your customers are asking AI. Explore the framework needed to make your brand the primary source for these powerful new engines.
Generative Engine Optimization (GEO) is the strategic process of making your brand's content the preferred source material for AI systems like ChatGPT and Gemini to cite in their responses. It moves beyond influencing ranking algorithms to directly influencing the knowledge base of generative AI. Unlike traditional SEO, which centers on Google's PageRank signals like backlinks and keyword density, GEO focuses on a completely different set of trust signals.
The core difference is the shift from ranking signals to citation signals. AI models prioritize:
Data Density and Structure: They reward content that is rich with specific data, metrics, and clear sourcing, which they can easily parse and verify.
Author Credibility and Nuance: AI systems like Claude evaluate the comprehensiveness and multiple perspectives presented in a piece, favoring nuanced writing over simplistic articles.
Recency and Freshness: Models such as Perplexity and Gemini integrate freshness signals, giving weight to the most current and relevant information.
This means a website with excellent Google rankings might never get cited if its content structure is not aligned with what AI engines trust. Learn how to re-engineer your content to meet these new criteria.
A genuine Generative Engine Optimization (GEO) strategy is focused on a systemic outcome: making your brand the primary cited source by AI for your customers' questions. In contrast, 'optimization theater' involves selling isolated, low-impact AI tactics that look modern but do not drive customer acquisition. The key difference lies in strategic depth versus superficial application.
A real GEO partner focuses on foundational work:
They measure the actual questions your customers ask AI, not just guess keywords.
They reverse-engineer the citation preferences of different models like ChatGPT and Claude.
They build a content system designed for AI ingestion, not just human readers.
In contrast, superficial services include 'ChatGPT content writing' (copy-pasting output), 'AI-powered analytics' (using an off-the-shelf tool without strategy), or 'prompt engineering workshops'. These tactics fail because they ignore the core challenge, which is not about using AI, but about influencing AI. Ask potential partners how they measure AI-driven leads, just as Lendingkart did to track growth from 40 to 228 leads.
The Lendingkart case demonstrates a direct and powerful impact on customer acquisition by shifting focus from Google to AI-driven channels. Despite spending Rs 12L per month on performance marketing that generated 40 high-quality leads, their win rate was declining. The pivot to a GEO strategy transformed their results without increasing ad spend.
By rebuilding their content specifically for citation by AI systems, Lendingkart achieved a significant market outcome. The key impact was a nearly 6x increase in lead volume, growing from 40 leads to 228 within six months. This was not just about more leads; it was about better leads. Customers began their sales conversations by stating, "ChatGPT recommended you," indicating a higher level of pre-sold trust and intent. This shift proves that the source of a lead profoundly affects its quality. A recommendation from a trusted AI carries more weight than a click on a paid ad, leading to a more efficient sales cycle. Discover how this change in discovery mechanism can reshape your own acquisition funnel.
The evidence is in the changing behavior of decision-makers, who now bypass traditional search for direct, synthesized answers from AI. A product manager evaluating marketing automation platforms or a healthcare professional researching drug interactions is not scrolling through Google results. They are asking Claude or Perplexity for a direct, consolidated recommendation. In this context, if your brand is not part of that AI-generated summary, it does not exist for that buyer at that critical moment of consideration.
This invisibility is confirmed by market data and client experiences:
Channel Shift: After implementing a GEO strategy, Lendingkart found that ChatGPT became their largest inbound channel, proving that this is where their audience was making decisions.
User Adoption: The rapid growth of platforms like ChatGPT, which hit 200 million weekly active users, shows a massive migration of research activity away from traditional search engines.
High-Intent Queries: The nature of questions asked to AI are highly specific and purchase-oriented, such as "best B2B SaaS marketing agency in India." Not being cited means you are excluded from the final consideration set.
Failing to appear in these responses is no longer a matter of ranking lower; it is a matter of complete omission from the buyer's journey. Learn how to secure your brand's position within these crucial new discovery platforms.
The Lendingkart case provides clear evidence that the lead source is a critical determinant of its quality and conversion potential. Before implementing a GEO strategy, the company was generating 40 leads from performance marketing but experienced a dropping win rate. This suggests the leads, while seemingly qualified, lacked the necessary trust or intent to convert efficiently.
After optimizing their content for AI citation, the new leads came in with a powerful form of social proof, telling the sales team, "ChatGPT recommended you." This simple statement reveals a fundamental shift in the buyer's mindset. An AI recommendation functions as a trusted, third-party endorsement, pre-framing the brand as an authoritative solution. This is distinctly different from a lead acquired via a Google ad, where the user knows the placement was paid for. The result for Lendingkart was not just a 470% increase in lead volume but an improvement in the inherent quality of each lead, which is a leading indicator of a higher win rate. This proves that a GEO-sourced lead enters the sales funnel with more momentum.
For a B2B SaaS company, the first step is to stop guessing what customers ask and start measuring it systematically. A successful GEO strategy begins with deep insight into the conversational queries that are relevant to your business. This requires a shift from keyword research to understanding user intent within platforms like ChatGPT and Claude.
Here is a three-step plan to begin:
Map Customer Pains to Conversational Queries: Start by interviewing your sales and customer success teams to identify the top 10-15 problems customers are trying to solve with your product. Translate these problems into the full-sentence, conversational questions a person would ask an AI, such as, "How can I reduce customer churn for my subscription service using analytics?"
Analyze Existing AI Responses: Input these mapped queries into ChatGPT, Claude, and Gemini. Document which companies, data points, and content formats are currently being cited. This competitive analysis reveals the content standards you need to surpass.
Identify Content Gaps for Data-Rich Assets: Based on your analysis, identify where your existing content falls short. The goal is to create new, data-dense assets or enrich existing ones with the specific metrics, case studies, and structured information that AI systems prioritize for citation.
This foundational work moves you beyond generic content creation and toward building assets engineered to make your brand the definitive answer. Explore how a full GEO operating system can scale this process.
As AI models like Gemini 2.0 become inseparable from the core Google search experience, the distinction between SEO and GEO will blur, but the strategic priority must shift toward GEO. By 2026, a significant portion of what is now considered SEO budget should be reallocated to initiatives that directly influence AI models. Continuing to invest heavily in old-school link-building or keyword-stuffing will yield diminishing returns.
Leadership should plan for a phased budget reallocation based on these future realities:
Synthesized Answers Dominate: More search results will be direct AI-generated answers, not lists of blue links. Budget must shift from 'ranking' to 'being the cited source' within these answers.
Content as a Data Asset: Investment should prioritize creating highly structured, data-rich content that serves as a reliable source for AI, rather than just human-readable blog posts. Think of it as building a proprietary knowledge base for AI to learn from.
Technical GEO Emerges: A portion of the budget will need to be allocated to the technical aspects of making content easily parsable and verifiable by AI, analogous to technical SEO today.
Companies like Lendingkart already prove that AI is a primary acquisition channel. Forward-thinking leaders should begin this strategic shift now, treating GEO not as an experiment but as the future foundation of digital discovery.
The most common mistake companies make is buying tactical AI tools instead of a comprehensive strategy, a trap known as 'optimization theater.' They are sold on services like 'AI-written content' or 'chatbot implementation,' which are isolated features that do not impact customer acquisition. These tactical offerings fail to address the fundamental shift in how customers discover brands.
To avoid this, you must look for a partner who can demonstrate a deep understanding of the AI ecosystem itself. Strong partners avoid common pitfalls by focusing on what matters:
Mistake: Focusing on writing content *with* AI. Solution: A real GEO partner focuses on writing content *for* AI systems to cite, understanding the unique formatting and data density each model like Claude or ChatGPT prefers.
Mistake: Guessing customer queries. Solution: An effective partner uses specific methods to measure the actual conversational questions your audience is asking AI, not just adapting old keyword lists.
Mistake: Offering a one-size-fits-all AI solution. Solution: A strategic partner has a proprietary methodology, like the GEO operating system, that systematically turns your brand into a citable authority.
Ask potential agencies to show you case studies, like the Lendingkart example where leads grew from 40 to 228, that prove they can generate revenue, not just reports.
A decline in win rates despite strong Google rankings is a classic symptom of a channel shift, where your ideal customers are using new discovery tools that you are not optimized for. To diagnose if a lack of AI visibility is the root cause, you need to assess your presence where modern research happens. The problem is not your existing channels; it is the new channels you are absent from.
Follow these diagnostic steps:
Conduct a Brand Citation Audit: Formulate 15-20 high-intent questions your customers would ask about your industry or product category. Ask these questions to ChatGPT, Claude, and Perplexity. Methodically log whether your brand, your content, or your competitors are cited in the responses.
Interview Recent Customers and Lost Deals: Ask new customers how they first heard about you. Specifically ask if they used AI tools in their research. For lost deals, inquire about the solutions they chose and the resources they used to evaluate them.
Analyze Competitor Citations: When competitors are cited in the AI audit, analyze the specific content being referenced. Deconstruct its structure, data density, and sourcing to understand why AI models prefer it over your own.
This process will quickly reveal if you have an AI visibility gap. The insights gained are the first step toward building a GEO strategy that recaptures those high-intent buyers.
The primary reason is that Google's PageRank and AI's citation algorithms value fundamentally different signals. A well-researched article can rank on Google through strong domain authority, backlinks, and keyword optimization, yet be completely ignored by an AI if its content is not structured for machine readability and verification. AI systems are not trying to rank a page; they are trying to extract a verifiable fact or a confident recommendation.
This misalignment occurs because AI models like ChatGPT and Claude heavily prioritize:
Structured Data: They need content formatted with clear headings, lists, and tables that allow them to easily parse and synthesize information. A narrative blog post, even if insightful, is less useful.
Explicit Sourcing and Data Density: AI rewards content that contains specific metrics (like Lendingkart's lead growth from 40 to 228) and clearly cites its sources, as this builds confidence in the information's accuracy.
Comprehensive Coverage: Models favor content that explores a topic from multiple perspectives, demonstrating true expertise, rather than a narrow, keyword-focused piece.
A structured Generative Engine Optimization (GEO) approach solves this by re-architecting your content to meet these specific machine-readable criteria, ensuring your expertise is not just seen by humans but also understood and cited by AI.
A successful content strategy must be multifaceted, creating assets that appeal to the distinct evaluation criteria of different AI models. You cannot use a one-size-fits-all approach; instead, you should create a portfolio of content designed for specific AI engine preferences. This means balancing data-rich assets with comprehensive, thought-provoking analysis.
To be cited by both ChatGPT and Claude 3, your strategy should include:
For ChatGPT: Develop content rich with hard data, statistics, and clear sourcing. This includes original research reports, benchmark studies, and detailed case studies like Lendingkart's, which grew leads from 40 to 228. This content should be highly structured with tables and lists for easy data extraction.
For Claude 3: Create comprehensive guides and articles that explore topics with significant nuance and present multiple perspectives. This content should demonstrate deep expertise and thoughtful analysis, going beyond surface-level explanations to address the 'why' behind the 'what.'
The ideal approach is often a hybrid, where a single, pillar piece of content contains both quantitative data sections and qualitative, nuanced discussion. This allows you to serve the needs of multiple AI systems simultaneously, maximizing your visibility across the platforms your customers use.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.
Growth Strategy and Planning
