Looks like you're in . Want the local site?
When someone asks ChatGPT, Gemini, or Perplexity a question that your business should answer, there are three distinct reasons you might not appear — and three distinct disciplines to fix each one. AEO, GEO, and LLMO are not the same thing. They target different layers of the AI search stack, and treating them as interchangeable is the reason most Nepali businesses run AI visibility experiments that go nowhere.
You are new to AI search entirely. Your team has heard that "ChatGPT is changing search" but has no structured view of what that means for your specific business, your specific customers, or your specific content. You need orientation before investment.
You have tried one approach and it has not worked. Perhaps you added FAQ schema to your website and expected to appear in AI answers. Perhaps you published a few thought-leadership articles hoping AI engines would start citing you. Each of those is one discipline in isolation — and isolation is exactly why it underperforms.
You are a marketing team that needs to brief leadership. AI search investment is a harder sell inside a Nepali organisation than traditional SEO, because the category is newer and the measurement frameworks are less familiar. You need a clear explanation of what AEO, GEO, and LLMO are, what each costs, and what each produces — in language that a CFO or board member can evaluate.
You are a business that operates across Nepal and internationally. Kathmandu-based companies serving both domestic and diaspora markets need AI visibility that works in multiple languages and on the AI engines used across different geographies.
Most content about AI search uses AEO, GEO, and LLMO interchangeably, or treats one as a catch-all label for the entire space. This produces briefs that are incoherent, strategies that target the wrong layer, and results that are impossible to attribute. A Nepali business that is optimising for AI citations without fixing its entity data is doing GEO work on a broken LLMO foundation. It will not hold.
AI engines are building their understanding of every industry right now. The businesses that are building structured data, authority signals, and LLM-readable content today are setting the baseline that AI engines will reference for the next several years. Waiting until AI search "matures" is not caution — it is ceding ground to competitors who are already building it.
AEO without GEO means your brand answers specific questions but never gets named when AI engines summarise your industry. GEO without LLMO means AI engines mention your brand but describe it inaccurately, or confuse you with a competitor. LLMO without AEO means your entity data is clean but you have no content for AI engines to surface in answer contexts. The three are interdependent.
Most AI training data and citation signals originate from English-language, Western-centric sources. A Nepali brand with no deliberate AI visibility programme is effectively invisible to the AI engines that growing numbers of Nepali consumers and B2B buyers are using as their first point of research.
is the discipline of making your content the direct answer to specific questions that your customers are already asking AI engines. This means mapping the exact questions in your market, structuring content so AI engines can extract a clear, citable answer, and implementing FAQ and HowTo schema so that answer is machine-readable. AEO targets the moment a specific question is typed or spoken — it is the most direct path from customer intent to brand appearance.
is the discipline of building the authority signals that cause AI engines to name your brand when answering broad industry questions — the kind that do not have a single right answer. When someone asks an AI engine "who are the best growth engineering companies in Kathmandu" or "which Nepali firms are doing serious work in digital growth," GEO determines whether your name appears in that answer. GEO is built through systematic citation signal development: earning references on high-authority external sources, monitoring where AI engines are drawing industry summaries from, and placing your brand consistently within those source pools.
is the discipline of shaping how AI models understand and represent your brand as an entity. LLMs do not just cite sources — they hold internal representations of companies, people, products, and industries. If that internal representation is incomplete, inaccurate, or absent, the LLM may describe your company incorrectly, conflate you with a competitor, or simply omit you from responses where you should appear. LLMO fixes this through structured entity data, knowledge gap identification, and content written specifically for LLM comprehension rather than traditional search crawlers.
AEO, GEO, and LLMO target three different layers of AI search visibility. AEO focuses on getting your content selected as the direct answer to a specific question — it works at the individual query level, through structured content and schema markup. GEO focuses on getting your brand named when AI engines answer broad industry questions — it works at the brand authority level, through citation signals and external references. LLMO focuses on how AI models internally represent your brand as an entity — it works at the knowledge model level, through entity data and LLM-optimised content. A business can have strong AEO and weak LLMO, meaning it wins specific answers but is described inaccurately when AI engines discuss it in broader contexts. All three layers need to work together for consistent AI visibility.
The right starting point depends on your current baseline, not a fixed rule. If you have no structured content and no entity data, the fastest first win is usually an LLMO foundation combined with targeted AEO content for your highest-intent questions. If you already have structured content but are not being named in industry summaries, GEO work on citation signals will produce results faster. The AI Search Audit in Step 1 of our process answers this question specifically for your business, so you are not guessing.
Most businesses will eventually need all three, but not necessarily simultaneously. Each discipline produces diminishing returns without the others — AEO content that is not supported by GEO citation signals is harder for AI engines to trust as authoritative, and GEO brand mentions that land on a weak LLMO foundation result in AI engines naming your brand but describing it incorrectly. The question is not whether you need all three, but in what order and at what pace to build them given your budget and business priorities.
AEO results — appearing in direct answers to specific questions — can begin appearing within four to eight weeks of content and schema implementation, assuming the questions targeted have existing AI search volume. GEO results — appearing in broad industry summaries — typically take three to six months because they depend on building citation signals across external sources, which takes time to accumulate. LLMO results — improvements in how AI models represent your brand — are the hardest to observe directly and typically show up in the LLMO accuracy score over a three-to-six-month period as model training cycles incorporate updated data. The programme is designed to produce AEO wins early while GEO and LLMO build in the background.
Pricing for a full integrated programme in the Nepal market starts at approximately NPR 150,000 per month, depending on the scope of the audit, the number of target questions and citation sources, and the complexity of the entity data work. Individual discipline-specific engagements (for example, an AEO-only programme for a business that already has strong GEO and LLMO foundations) are available at a lower entry point. The AI Visibility Audit is the starting point for all programmes and is priced separately so you understand your baseline before committing to an ongoing programme.
Traditional SEO and AI search optimisation share some foundational inputs — structured content, clean site architecture, and authoritative external references are important for both — but they diverge significantly in how they produce visibility. Traditional SEO optimises for position in a ranked list of links. AEO, GEO, and LLMO optimise for inclusion in AI-generated text responses that may not display traditional search results at all. As AI-generated answers become the first point of contact for a growing share of searches, the businesses that have built AI visibility alongside their traditional SEO rankings will have a compounding advantage. We recommend treating AI search optimisation as a parallel programme, not a replacement for traditional SEO.
Not a black box. Real specialists you can call, with their names on the work.
The AI search audit takes less than two weeks and gives you a clear baseline across all three disciplines — AEO citation rate, GEO brand mention share, and LLMO accuracy score. From that baseline, you will know exactly which discipline to prioritise and what the opportunity is worth.
Run an AI Visibility Audit
Ignited Nepal
Growth engineering · replies within 1 business day
By continuing you agree to our privacy policy.
