How to Get Your Business Recommended by ChatGPT, Gemini and Perplexity

Three AI Tools, Three Different Discovery Mechanisms — One Unified Strategy

The most common mistake businesses make when approaching AI visibility is treating all AI tools as if they work the same way. They do not. ChatGPT recommending a Nairobi accountant is a fundamentally different process from Perplexity recommending a Nairobi accountant — and both are different from Gemini doing the same. Getting recommended by all three requires understanding each one separately and then building the overlapping signals that satisfy all of them.

Here is the single most important thing to understand about each platform before we go into the detail:

ChatGPT recommends based on what it has already learned. Its primary knowledge source is a training dataset — a massive snapshot of the internet built over years of web crawling. When ChatGPT recommends a business, it is drawing from patterns in that training data: which businesses were mentioned frequently, in what contexts, with what level of credibility, across what range of sources. Getting into ChatGPT’s recommendations is largely a historical content and citation challenge — it takes time, and it rewards businesses that have been producing credible content and earning external mentions over an extended period.

Perplexity recommends based on what it finds right now. It is a real-time search engine with an AI synthesis layer. When you ask Perplexity to recommend a business, it searches the web, finds relevant current pages, and synthesises an answer from what it finds — citing its sources explicitly. Getting into Perplexity’s recommendations is largely a current content quality challenge — fresh, specific, well-structured content published recently is what Perplexity draws from.

Gemini recommends based on Google’s data ecosystem. It integrates Google Search, Google Maps, Google Business Profile, Google Knowledge Graph, and Google’s web index. Getting into Gemini’s recommendations is largely a Google ecosystem optimisation challenge — the same signals that drive Google Search performance, local search visibility, and Google Business Profile completeness drive Gemini recommendations.

Understanding these three distinct mechanisms is the foundation of the GEO Stack. The strategy is not to do three separate things — most of the signals that build ChatGPT citation authority also help Perplexity and Gemini. But the priority order and the specific tactical focus differ by platform, and knowing which lever to pull first for each platform is what makes the strategy efficient.

This article is part of the Visibility Engine knowledge cluster. It builds on three foundational articles: entity authority, E-E-A-T signals, and schema markup. Read those first if you have not already — this article assumes their content as background.

Platform by Platform: How ChatGPT, Gemini, and Perplexity Each Discover Your Business

ChatGPT: The Training Data Challenge

ChatGPT’s primary knowledge source is its training dataset — a massive collection of text from across the internet, assembled through web crawling over multiple years and periodically updated. When ChatGPT makes a recommendation, it is drawing from patterns learned during training: which entities were mentioned in which contexts, which brands were associated with which qualities, which businesses were cited as examples of their category.

The implications for businesses seeking ChatGPT citations:

Historical web presence matters enormously. A business that has been producing and distributing quality content for three years has significantly more training data presence than one that started six months ago — even if the newer business has better content. This is not entirely fixable quickly, but it is buildable systematically. Every piece of content published, every external mention earned, every directory listing created adds to the training data footprint that benefits the next model update.

Content distribution multiplies training data presence. A blog post published only on your own website reaches your website’s audience. The same content summarised as a LinkedIn article, referenced in an industry newsletter, linked to from a forum discussion, and cited in another website’s article has reached multiple training data sources. Research from Stacker found that distributing content to external platforms can increase AI citation probability by up to 325% compared to publishing only on your own site. For ChatGPT specifically — where training data breadth is the primary coverage mechanism — this distribution effect is the most direct way to build citation presence.

Third-party mentions outweigh self-published content. ChatGPT, trained on the web’s content ecosystem, has absorbed the web’s implicit trust hierarchy: third-party sources are more credible than self-promotional content. A business mentioned by name in a credible industry publication carries more ChatGPT citation weight than the same business describing itself on its own website. The external citation building in every industry guide in this cluster — press mentions, directory listings, professional association memberships — is building ChatGPT training data presence as much as it is building E-E-A-T authority.

ChatGPT browsing capability changes the calculus for browsing-enabled queries. When a user enables browsing or uses ChatGPT with web access, ChatGPT can retrieve real-time web results via Bing — similar to Perplexity’s mechanism. For browsing-enabled queries, current web content quality matters as directly as it does for Perplexity. This is an increasingly common usage pattern, particularly for business and professional queries.

Perplexity: The Real-Time Content Challenge

Perplexity is architecturally a search engine with an AI synthesis and citation layer on top. When you submit a query, Perplexity performs a web search, retrieves a set of relevant current pages, reads them, synthesises an answer, and cites its sources explicitly — with URLs and source names displayed alongside the generated text.

The implications for businesses seeking Perplexity citations:

Recency is the dominant signal. Perplexity’s search retrieval mechanism favours recently published, recently updated content. A well-structured article published last month will frequently outperform a better-ranked article published two years ago for Perplexity citations. This makes Perplexity the fastest path to AI citation for businesses willing to publish fresh, specific content consistently — and the most punishing platform for businesses with static, outdated websites.

Direct answers win over comprehensive coverage. Perplexity’s synthesis layer works best with content that directly answers the query in an extractable passage. The same direct answer structure that the T4 guide recommends for Google AI Overviews — answer in the first paragraph, short extractable paragraphs, FAQ sections — applies equally to Perplexity citation optimisation. The difference is that Perplexity’s citation volume tends to be higher per query (it typically cites four to eight sources) and its recency filter is more aggressive.

Perplexity heavily indexes Reddit, forums, and community content. Unlike Google, which applies strong quality filters that tend to suppress user-generated content, Perplexity draws actively from Reddit discussions, Quora answers, industry forums, and community platforms. A business that has genuine advocates discussing it in relevant online communities — not planted content, but organic community mentions — benefits from Perplexity citations that traditional SEO cannot capture. For Kenyan businesses, relevant platforms include Reddit’s Kenya and Africa subreddits, local business forums, professional LinkedIn groups, and any community where your target customer discusses your category.

Perplexity Pro and its research mode use multiple search passes. For complex queries, Perplexity Pro performs multiple rounds of search and synthesis — broadening its source base with each pass. Deep, comprehensive content that addresses multiple angles of a complex topic performs well in these multi-pass queries because it remains relevant across different sub-questions within the research session. Content clusters — the architecture used throughout this cluster — are specifically well-suited to multi-pass Perplexity research queries.

Gemini: The Google Ecosystem Challenge

Gemini is Google’s AI assistant — and it has privileged access to Google’s entire data ecosystem. Google Search ranking data, Google Maps business listings, Google Business Profile information, Google’s Knowledge Graph entity data, Google Reviews, Google Shopping data, and Google’s web index all feed into Gemini’s responses in ways that ChatGPT and Perplexity cannot replicate.

The implications for businesses seeking Gemini citations:

Google Business Profile is the primary Gemini signal for local and service businesses. For queries with local intent — “best accountant in Nairobi,” “dentist in Karen,” “packaging supplier in Mombasa” — Gemini draws primarily from Google Business Profile data. A business with a complete, well-reviewed, actively managed Google Business Profile appears in Gemini local recommendations in ways that content optimisation alone cannot achieve. Category selection, service listings, Q&A management, photo quality, review volume and recency — these Google Business Profile signals are Gemini recommendation signals.

Google Search rankings feed Gemini’s knowledge base directly. Gemini’s web knowledge layer draws from Google’s search index — the same content that ranks well in Google Search is the content Gemini can access for non-local, non-Maps queries. This creates the strongest possible alignment between Google SEO investment and Gemini citation: a business that ranks well in Google for its target queries will generally appear in Gemini recommendations for those same queries, provided its content is well-structured and E-E-A-T compliant.

Google Knowledge Graph entity recognition amplifies Gemini citation. Google maintains a structured knowledge base — the Knowledge Graph — of entities it has identified as real-world things: businesses, people, places, organisations, products. A business that has achieved Knowledge Graph entity recognition — which results from consistent entity signals across the web, complete schema, and cross-platform identity consistency — benefits from Gemini treating it as a known, verifiable entity rather than an anonymous web result. The entity authority work covered in the Entity Stack guide directly supports Knowledge Graph recognition.

Google Reviews are Gemini’s primary trust signal for service businesses. For service business recommendations specifically — “which [type of professional] should I use in [location]?” — Gemini draws heavily from Google Reviews data. Review volume, recency, and rating are primary signals. Review content specificity — reviews that mention specific services, outcomes, and named staff members — contributes to Gemini’s ability to match your business to specific service queries. The review strategy recommended throughout the industry guides in this cluster (systematic review requests, prompting specificity) is Gemini optimisation as much as local SEO.

The Generative Engine Optimisation Stack: Five Layers for Cross-Platform AI Citation

Layer 1: Training Data Presence — Building the Long-Term ChatGPT Foundation

Training data presence is the foundation of ChatGPT citation authority and a significant contributor to Gemini’s knowledge base. It is built through consistent content publication and external distribution over time — there is no shortcut, but there is a compounding effect that means early movers benefit disproportionately.

Content publication history. Every substantive article published on your website adds to your training data footprint. The key word is substantive — thin, duplicate, or generic content adds noise rather than signal. A focused library of specific, expert-authored, well-structured articles on your core expertise area builds topic association in AI training data: “this business is an authority on [topic X]” is the pattern that produces confident AI recommendations for [topic X] queries. Quantity matters less than consistency and quality — one excellent article per month for two years outperforms ten mediocre articles published in a single sprint.

External distribution of every piece of content. The training data footprint of a piece of content is proportional to how many credible external sources it appears in, links to it, or references it. For every article you publish on your website:

• Publish a summary version as a LinkedIn article (LinkedIn is among the most heavily indexed professional content sources in AI training data)
• Share the key insight or statistic as a standalone LinkedIn post with a link
• Submit the piece to relevant industry newsletters or aggregators for inclusion
• Reference it in responses to relevant questions on Quora, Reddit, or industry forums — genuinely helpful responses, not spam
• Pitch the core insight as a contribution to a relevant industry publication

Each distribution point creates an additional indexed reference to your content and your business name in the AI training data ecosystem. The cumulative effect over months and years is the broad-web presence that makes ChatGPT recognise and recommend your business confidently.

Wikipedia and knowledge base presence. For businesses or individuals who have achieved sufficient notability, a Wikipedia page or Wikidata entry is one of the strongest possible training data presence signals — Wikipedia is heavily weighted in AI training data, and Wikidata entities are directly integrated into several AI knowledge graph systems. This is not achievable for most small businesses. But for industry figures, well-known companies, or niche market leaders with documented public presence, pursuing Wikipedia notability is a high-value long-term investment in AI training data authority.

Layer 2: Real-Time Citation Signals — Building the Perplexity Fast-Track

Real-time citation signals are the Perplexity-specific layer — the signals that determine whether fresh web content retrieval surfaces your business. They are also the ChatGPT browsing layer and contribute to Gemini’s current web knowledge. Building these signals means treating your website as a living publication, not a static brochure.

Publishing cadence — the most important real-time signal. Perplexity rewards recency. A website that publishes substantive, relevant content consistently — at minimum monthly, ideally fortnightly — maintains an active freshness signal that keeps it in Perplexity’s retrieval pool for relevant queries. A website that was last meaningfully updated six months ago is progressively deprioritised in Perplexity’s real-time search. Set a sustainable publishing schedule and stick to it. One focused, expert-authored article per month is more valuable than irregular publishing bursts.

Updating existing content is as valuable as creating new content. Perplexity does not distinguish between new articles and recently updated articles — both are fresh. When industry conditions, pricing, regulations, or best practices change in your field, update the existing articles that cover those topics. Add the updated date explicitly. Change the dateModified in your Article schema. This keeps your content library relevant and Perplexity-retrievable without requiring constant new article creation.

Structured data for content pages. Perplexity’s retrieval layer can read structured data and uses it to understand content type, authorship, and topic. Article schema with correct datePublished, dateModified, and author properties helps Perplexity correctly attribute your content’s freshness and authorship in its synthesis. FAQPage schema provides pre-formatted Q&A content that Perplexity can directly extract for answer synthesis. The schema investment covered in the schema guide serves Perplexity citation as directly as it serves Google.

Community platform presence. As noted in the platform section, Perplexity indexes Reddit, Quora, and industry forums heavily. Building a genuine, helpful presence in the online communities where your target customers discuss your topic area is both a brand awareness strategy and a Perplexity citation strategy. Helpful answers on relevant Quora questions — attributed to your real identity, with your website linked in your profile — create Perplexity-indexable community content that generic SEO cannot replicate.

Layer 3: Cross-Platform Entity Consistency — The Foundation All Three Platforms Rely On

Every AI platform — ChatGPT, Gemini, Perplexity, and Google AIO — performs some version of entity verification before making a confident business recommendation. The mechanism differs, but the requirement is the same: your business name, location, category, and key attributes must appear consistently across multiple independent sources so that AI can identify them as referring to the same entity and aggregate confidence from the consistency.

This is the cross-platform application of the entity authority principles in the Entity Stack guide — applied specifically to AI citation rather than search ranking. The practical checklist:

Business name consistency. One canonical version of your business name, used identically across: your website homepage and About page, Google Business Profile, LinkedIn Company page, Facebook page, Twitter/X handle, industry directories, press mentions, and any other public web presence. Any variation — “SEO Smart” vs “SEO Smart Ltd” vs “SEO Smart Kenya” — creates entity fragmentation. Pick your canonical name and enforce it everywhere.

NAP consistency. Name, Address, Phone — identical across all platforms. This is basic local SEO, but it applies equally to AI entity verification. Inconsistent phone numbers or address formats across your website, Google Business Profile, and directory listings signal entity fragmentation to AI systems attempting to aggregate your business’s information.

Category and description consistency. Your business category declared consistently — on Google Business Profile, on LinkedIn, on industry directories, in your schema — enables AI to confidently categorise you and match you to category queries. A marketing agency that is categorised differently on different platforms creates an ambiguous entity that AI has lower confidence recommending for category-specific queries.

Social media handle consistency. @seosmartltd on Instagram, TikTok, Facebook, LinkedIn, Twitter/X — consistent handles that reflect the canonical business name create cross-platform entity associations that AI training data and Gemini’s social integration layer both use for entity recognition.

Schema sameAs declarations. Your website’s Organization schema should declare all your major external profiles in the sameAs property — Google Business Profile URL, LinkedIn page, Facebook page, Twitter/X profile, relevant industry directory listings. This is the machine-readable version of cross-platform entity consistency — it explicitly tells every AI that reads your schema: all of these profiles are the same entity as this website.

Layer 4: Expert Content Architecture — The Content Structure That Earns Citations Across All Three Platforms

While the discovery mechanisms differ across ChatGPT, Gemini, and Perplexity, the content quality signals they look for in a citable source are remarkably consistent. All three platforms, trained on or retrieving from quality web content, have absorbed the web’s implicit standards for expert, trustworthy information. Meeting those standards is the content layer that makes cross-platform citation reliable.

Named expert authorship — universal requirement. Across all three platforms, content attributed to a named individual with verifiable expertise is more likely to be cited than anonymous corporate content. ChatGPT’s training data has a strong named-expert bias — quality publications credit named authors, and ChatGPT has learned to treat named authorship as a quality signal. Perplexity shows the author name in its citations. Gemini uses author entity data from Article schema. Named authorship is the single most consistently cross-platform content signal in the GEO Stack.

Direct answer structure — universal requirement. All three platforms synthesise answers rather than reproducing full articles. They extract the most relevant passages from source content. The direct answer structure described in the T4 AIO guide — answer in the first paragraph, short extractable paragraphs, FAQ sections — applies equally to ChatGPT browsing citations, Perplexity synthesis, and Gemini knowledge retrieval. This is not Google-specific optimisation. It is AI-readable content architecture.

Content cluster architecture — amplifies all three platforms. A content cluster — a pillar article linked to multiple supporting articles on related topics — builds topical authority signals that benefit ChatGPT training recognition, Perplexity multi-pass research queries, and Gemini topical knowledge. The cluster architecture used throughout this Visibility Engine cluster is a cross-platform AI citation strategy as much as a traditional SEO strategy. Building clusters around your primary expertise topics is the long-term content investment that produces compounding AI citation authority across all platforms simultaneously.

First-hand experience and specificity — increasingly critical. AI platforms trained on vast volumes of generic content have learned to discriminate between content with genuine first-hand specificity and content that synthesises and rephrases publicly available information. “In working with 47 Kenyan manufacturing companies on packaging specifications over the last three years, I have found that…” is specific, first-hand, and attributable. “According to experts, packaging specifications are important because…” is not. The former is more citable across all three platforms because it provides information that AI cannot generate from its own knowledge — real-world experience from a named expert.

Layer 5: Proactive Citation Building — The External Signals That Compound AI Authority Over Time

The first four layers build the internal foundations — your content, your entity consistency, your schema, your expert authorship. Layer 5 is the external signal layer — the activities that generate the third-party mentions, links, and references that AI training data, Perplexity’s retrieval, and Gemini’s entity knowledge all draw from as authoritative external validation.

Targeted external publication. Publishing articles, contributing expert commentary, or being quoted as a named source in publications that AI tools index highly is the most direct way to build cross-platform training data presence. Different platforms weight different publication types:

• For ChatGPT: Any publication with significant historical web presence — industry trade journals, established business publications, reputable online media. The older and more established the publication, the more likely it appeared in ChatGPT’s training data.
• For Perplexity: Recently published content in any publication with current Google indexation. Perplexity does not strongly discriminate by publication age — a recent article in a smaller but legitimate publication is more retrievable than an older article in a major publication.
• For Gemini: Publications that Google considers authoritative for the topic — typically publications with strong Google PageRank and established Google indexation. The Google News index is particularly relevant for business and professional topics.

Industry directory and professional association listings. Every credible directory listing is both an entity corroboration signal (for ChatGPT entity recognition and Gemini Knowledge Graph) and a Perplexity retrieval signal (directory pages appear in web search results). For Kenyan businesses: KAM, KEPSA, Kenya ICT Board, KUCCPS-listed institutions, EARB, KMPDC — whichever directories are relevant to your industry. Each listing creates an independently indexed mention of your business name and category that contributes to cross-platform entity confidence.

Structured media engagement. Providing expert quotes for journalists, participating in podcasts, appearing on YouTube channels with relevant audiences, contributing to industry reports — each of these generates indexed external content in which your name and your business are cited as a credible source. This is the E-E-A-T authoritativeness signal applied to AI training data building: the pattern of being cited as an expert across multiple independent sources is what AI models have learned to associate with genuine authority.

Monitoring and responding to existing citations. Once your GEO Stack investment begins producing AI citations, track them. When ChatGPT mentions your business, note the context and the query type — this tells you which content is generating the citation and which query category you have established authority in. When Perplexity cites your article, the citation shows you which passage was extracted — you can use that to improve the extractability of similar passages in related articles. Citation monitoring is not just measurement — it is a feedback loop that guides your content and distribution priorities.

Putting the GEO Stack Together: A 90-Day Action Plan

The GEO Stack is a long-term compound investment — but specific actions produce early results. Here is a practical 90-day sequence that builds across all five layers simultaneously:

Days 1–14 (Entity foundation). Audit your business name, address, and category across every platform where you have a presence. Standardise everything to one canonical version. Update your schema sameAs to declare all your external profiles. Confirm Google Business Profile is complete, accurate, and in the right category. These entity consistency fixes are the fastest actions with the broadest cross-platform impact.

Days 15–30 (Content audit and quick wins). Audit your existing published content. Identify the five to ten most important informational pages and apply direct answer structure retrofits: move the answer to the first paragraph, add question-format H2s, add a FAQ section with FAQPage schema. These structural retrofits on existing content produce Perplexity citation improvements within weeks and AIO improvements within one to two months.

Days 31–60 (Expert content publishing). Publish the first two pieces of new expert content in your priority query categories — named author, first-person experience, direct answer structure, FAQ section, full schema. Distribute each piece through LinkedIn, relevant community platforms, and any accessible external publications. These pieces start building training data presence immediately.

Days 61–75 (External citation outreach). Identify three to five industry publications, directories, or community platforms relevant to your business and make your first external citation investments: submit a guest article, ensure directory listings are complete, answer relevant questions on Quora or Reddit with genuine expertise. Each external mention multiplies the training data footprint of everything you have built so far.

Days 76–90 (Measurement and recalibration). Run your target queries through ChatGPT, Perplexity, and Gemini. Check Google Search Console for early AIO data. Note which queries are producing citations, which are not yet, and what the cited content looks like for the queries where you are not appearing. Use this data to prioritise the next 90 days of content and distribution investment.

The GEO Stack in the Kenyan Context: Why the Opportunity Is Uniquely Concentrated

The Generative Engine Optimisation opportunity in Kenya has a specific characteristic that makes it more accessible than in most markets: the competition for AI citation positions is extraordinarily low relative to the query volume.

When a Kenyan consumer asks ChatGPT about a Kenyan business, service, or topic, AI is drawing from a very thin pool of Kenya-specific, credible, well-structured content. The vast majority of the web’s quality content is written for Western markets, by Western businesses, in Western contexts. Kenya-specific queries — “best SACCO for small business owners in Kenya,” “how to buy a car in Kenya without being scammed,” “which Nairobi neighbourhoods are good for families” — return AI answers that either draw from very few Kenyan sources or generalise from non-Kenyan content inappropriately.

This means that a Kenyan business investing in the GEO Stack right now is not competing against hundreds of well-optimised competitors. It is often the first credible, well-structured, expert-authored source in its category for Kenya-specific queries. First-mover advantage in AI citation is particularly durable — AI citation patterns reinforce themselves as the cited content earns more external links, more community references, and more training data inclusion with each passing month.

The full picture of why now matters for Kenya is in the Kenya First-Mover article → The GEO Stack is the operational playbook for capturing that opportunity.

Five GEO Stack Mistakes That Leave AI Citations on the Table

Mistake 1: Optimising for One Platform Only

The most common GEO Stack mistake. A business that optimises exclusively for Google (and therefore benefits from Gemini through the Google ecosystem) while ignoring the distinct discovery mechanisms of ChatGPT and Perplexity is leaving a significant share of AI citation opportunities uncaptured. ChatGPT handles the largest volume of business recommendation queries globally. Perplexity is the fastest-growing AI search tool with the most explicit citation behaviour. Both require specific attention beyond Google ecosystem optimisation. The GEO Stack’s five layers are designed to address all three platforms simultaneously — not as three separate projects, but as one integrated investment that allocates effort appropriately across each platform’s specific requirements.

Mistake 2: Publishing Without Distributing

A blog post that lives only on your website reaches one source in the training data ecosystem. The same content distributed through LinkedIn, referenced in an industry forum, summarised in a newsletter, and linked from a directory profile reaches five or six sources. For ChatGPT citation specifically — where training data breadth is the primary coverage mechanism — distribution is as important as creation. Build a distribution checklist into your content publishing process: every piece of content goes through the same five to six distribution channels before it is considered complete. The content that earns the most AI citations is almost never the best-written piece — it is the best-distributed piece.

Mistake 3: Static Websites That Never Update

A website with its last meaningful content update more than six months ago is becoming progressively less relevant to Perplexity’s real-time retrieval. For Perplexity — the platform with the most aggressive recency weighting — a static website is an invisible website for time-sensitive queries. The fix is not a complete website overhaul. It is a sustainable publishing cadence: one new article per month, quarterly updates to key existing pages, and a clear schema-declared dateModified on every meaningful content update. Consistency over time beats irregular publishing bursts.

Mistake 4: Ignoring Community Platform Presence

Reddit, Quora, and industry forums are disproportionately represented in Perplexity’s source pool relative to their traditional SEO value. A business whose name is never mentioned in relevant community discussions — where real customers and professionals talk about its category — is missing a Perplexity citation source that cannot be replaced by any amount of website optimisation. Building a genuine community presence takes time and authentic engagement. But even modest, honest participation in relevant online communities — answering questions in your area of expertise, contributing to relevant discussions — generates the kind of organic community mentions that Perplexity surfaces in ways that traditional SEO cannot manufacture.

Mistake 5: No Measurement Process

The businesses that build the strongest AI citation authority over time are the ones that monitor their AI citation performance systematically and use that data to refine their strategy. Without measurement, you cannot know which content is generating citations, which query categories you have successfully entered, or what the gap is between your current citations and the ones you are targeting. Set up a monthly AI citation monitoring process: run your 15 most important target queries through ChatGPT, Perplexity, and Gemini. Record whether you appear, in what context, for which query type. Track this month over month. The data tells you where to invest next.

Ready to Build Your GEO Stack and Get Recommended Across All the Major AI Tools?

The businesses that will dominate AI-driven customer discovery over the next five years are not necessarily the biggest, the best-funded, or the longest-established. They are the ones that understood earliest how AI recommends businesses — and built systematically for it. The GEO Stack is that system. It is not a one-time project. It is an ongoing investment that compounds in value with every piece of content published, every external mention earned, and every entity signal strengthened.

At SEO Smart, we build and manage the complete GEO Stack for businesses across Kenya and globally — as part of our Visibility Engine service. If you want to know exactly where your business stands in ChatGPT, Gemini, and Perplexity today — and what it would take to become the recommended option in your category — let us talk.

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Mehul Shah

Mehul Shah is the Founder and Managing Director of SEO Smart Limited, a specialised SEO, GEO and AEO agency based in Kenya. With nearly 20 years of experience, Mehul helps agencies and businesses build scalable SEO strategies, performance-optimised websites, and conversion-driven content marketing frameworks.

www.seosmart.co.ke