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ChatGPT, Claude, and Gemini are becoming the first stop for research in Nepal's growing tech economy. If an LLM can't find accurate information about your brand, it won't mention you — and your competitors fill that space. Ignited Nepal runs the first LLMO readiness service built for Nepal-market brands.
You know your customers are starting to use AI tools for research and purchasing decisions, but you have no idea whether your brand appears in those results — or what those results say.
You're a CMO or growth lead who wants to get ahead of the LLM search shift before Nepal's market catches on. You understand that the brands structured for LLM citation now will hold that position for years.
Your buyers are already using Claude or ChatGPT to evaluate vendors. If an LLM describes your product inaccurately — or skips you entirely — you're losing deals you never see in your pipeline.
You typed your company name into ChatGPT and got silence, a wrong description, or a confident but inaccurate summary. You need to know how bad the problem is and what to do about it.
LLMs have knowledge cutoffs and rely on indexed entity data to represent brands. If your knowledge graph presence is thin, your Wikipedia or Wikidata records are missing, and your structured data is sparse, LLMs either won't mention you or will pull outdated information with confidence.
Long editorial articles and narrative brand copy are written for human readers. LLMs extract structured, factual, directly-claimable information. If your pages bury key facts in prose, LLMs pass your content over in favour of more extractable sources.
Different spellings of your business name, conflicting descriptions, mismatched categories across directories, social profiles, and press coverage confuse LLM entity matching. The model can't confidently resolve "which brand is this?" — so it defaults to a competitor it can resolve clearly.
You can't improve what you can't see. Most brands have no record of what any LLM currently says about them, which means there's no way to know whether anything is getting better. LLMO without monitoring is guesswork.
A scored review of your entire entity footprint: structured data completeness, content extractability, citation signals, and knowledge graph presence. Benchmarked against your category competitors.
We test 12 LLMs with queries about your brand and category. The report maps what they currently know, what they get wrong, and what they don't know at all — so we have a precise target for every improvement.
Wikipedia, Wikidata, schema markup, and web-wide entity cleanup built for LLM accuracy. This is the foundational layer that determines whether LLMs can reliably identify and cite your brand.
Page-by-page recommendations for restructuring your existing content so key facts, differentiators, and claims are directly extractable by language models. Includes new content briefs where gaps exist.
A branded scorecard that quantifies your current LLM visibility against category benchmarks. Tracks improvement month over month as work is implemented.
Ongoing tracking of what 12 LLMs say about your brand across brand, category, and competitor query sets. Accuracy improvement measured month-on-month with a structured report.
LLMO readiness is a measure of how well a brand's content, entity data, and structured information are organised for accurate citation by large language models. It covers the quality of your knowledge graph presence, the extractability of your web content, the consistency of your entity data across sources, and whether LLMs currently represent your brand accurately.
AI tools including ChatGPT, Claude, and Gemini are used by researchers, buyers, and decision-makers in Nepal and globally. If your brand is absent or inaccurate in those responses, you miss visibility in a channel that is growing faster than traditional search. LLMO readiness positions Nepal-market brands ahead of this shift.
We run a structured query set across 12 LLMs, covering brand queries, category queries, and comparative queries. Responses are logged, scored for accuracy, and mapped against what your brand should say. This baseline is the starting point for all LLMO work.
Entity data structuring covers Wikipedia presence, Wikidata records, JSON-LD schema markup on your website, and consistency checks across directories, social profiles, and press. The goal is for LLMs to reliably identify your brand as a specific, well-described entity they can cite accurately.
The initial LLMO Readiness Report is delivered in 5 business days. Knowledge gap mapping and entity structuring run through weeks 2–4. Content optimisation runs across months 1–3. Ongoing monthly monitoring starts after the initial assessment.
LLMO readiness engagements for Nepal-market brands start from NPR 85,000 for the initial assessment and knowledge gap report. Monthly monitoring and entity structuring are scoped after the assessment based on gap size and brand complexity. Contact hello@ignitednepal.com for a specific quote.
LLMO and traditional SEO share some foundations — structured data, content clarity, entity consistency — but they are separate disciplines. Improving LLMO readiness often has a positive effect on structured data quality across both channels. We assess both as part of the AI Visibility Audit.
LLMO (Large Language Model Optimisation) is the most specific term for optimising brand visibility within LLM responses. AEO (Answer Engine Optimisation) and GEO (Generative Engine Optimisation) overlap significantly. All three are part of Ignited Nepal's AI visibility engineering practice, with LLMO focused specifically on LLM entity accuracy and citation quality.
The brands that structure their entity data, content, and knowledge graph presence for LLM accuracy now will be the brands LLMs cite as category references in two years. LLMO readiness is not a future project — every month without a baseline is a month your competitors can close the gap.
Ignited Nepal — Growth Engineering Company. Working with Nepal-market brands across tech, SaaS, and professional services.