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ChatGPT, Perplexity, and Gemini are describing Nepali businesses to international buyers right now. If those descriptions are wrong — outdated, conflated with a competitor, or missing entirely — you are losing opportunities before a single conversation begins. We audit what AI engines say about your brand and correct it at the source.
The wrong description — You searched your business name in ChatGPT and found the wrong founding year, wrong location, or services you stopped offering three years ago. International buyers see that description before they ever visit your website.
Conflated with a competitor — When buyers ask AI about your category in Nepal, your brand gets lumped together with a competitor or a similarly named company. The AI cannot tell you apart, and your distinct positioning is lost.
Describing the old version of you — Your business has evolved — you pivoted, rebranded, or expanded your services — but AI engines still describe what you used to be. The authoritative sources they draw on were never updated.
You have no idea what AI says — You have never checked what ChatGPT or Perplexity says about your business. For Nepali exporters, trekking operators, and handicraft suppliers, that is a significant blind spot — international buyers are researching you right now.
ChatGPT draws on web data that was crawled months or years ago. If your Wikipedia page, Crunchbase entry, or early news coverage contains errors, those errors propagate into every AI response that references your brand — and they compound over time.
If your brand name overlaps with another company, AI models will blend the two entities. Buyers asking about your trekking company get facts about a different operator in the same region. This is an entity disambiguation problem that requires deliberate correction across structured data sources.
Your business is different now. But the sources AI engines trust most — Wikipedia, Wikidata, established media — reflect the older version. No one has pushed through a correction, so the AI keeps describing a company that no longer exists in that form.
Smaller or newer Nepali businesses often have a thin web presence: no Wikipedia article, minimal structured data, few authoritative mentions. When AI engines cannot find enough reliable information, they either say nothing or fabricate plausible-sounding details. Both outcomes damage buyer trust.
We query 12 AI engines — including ChatGPT, Perplexity, Gemini, Claude, and Copilot — using your brand name, competitor comparisons, and category queries. Every response is documented. Every inaccuracy is flagged. You receive a complete Brand Answer Audit Report.
Every inaccuracy is categorised and diagnosed. We identify where the AI is sourcing wrong information — which web page, which outdated record, which crawled document — so corrections can be targeted and permanent.
We update the authoritative records that AI engines draw on: Wikipedia, Wikidata, Crunchbase, and official business directories. Corrections are made to reflect your current business accurately, not the version that existed years ago.
Accurate brand mentions in tier-1 media, trade publications, and analyst references give AI engines reliable, citable information about your brand. We earn placements that LLMs draw on when generating brand answers.
We build the structured data signals that allow AI engines to distinguish your brand from similarly named competitors — separate entities with separate attributes, clearly differentiated across the web.
Every month, we re-audit your brand answers across 12 AI engines, track accuracy improvement over time, and deliver a Monthly Brand Accuracy Report. You see exactly how AI descriptions of your brand are changing.
Brand answer accuracy is the degree to which AI engines — ChatGPT, Perplexity, Gemini, and others — describe your business correctly when asked about it. Inaccuracies range from wrong founding dates and locations to outdated service descriptions and confusion with competitors.
AI engines draw on web data that was crawled at various points in the past. If authoritative sources — Wikipedia, Crunchbase, early news articles — contain errors or outdated information, the AI treats those as reliable and repeats them. Without active correction, inaccuracies persist indefinitely.
International buyers — particularly those sourcing trekking services, handicrafts, or export goods — often research suppliers through ChatGPT before making contact. An inaccurate AI description can disqualify a Nepali supplier before any direct communication occurs.
We query 12 AI engines using your brand name, competitor comparison prompts, and category-level queries. Every response is documented. Every inaccuracy is identified and categorised. You receive a complete report with a hallucination log and source diagnosis.
We correct the source records that AI engines draw on — Wikipedia, Wikidata, Crunchbase, and official directories — and earn accurate brand mentions in authoritative media that LLMs reference. This changes the underlying data, not just the surface output.
Source corrections typically begin reflecting in AI responses within four to twelve weeks, depending on the engine and how frequently it refreshes its training or retrieval data. Some engines update faster than others.
Pricing is available on request and depends on audit scope, the number of inaccuracies requiring correction, and the scale of authoritative source building required. Contact us at hello@ignitednepal.com for a scoped proposal.
Yes. Monthly monitoring covers re-auditing your brand answers across 12 engines, tracking accuracy improvement over time, and flagging new inaccuracies in a Monthly Brand Accuracy Report.
If international buyers are researching Nepali suppliers through ChatGPT, Perplexity, or Gemini, the description they receive is shaping their shortlist before they ever contact you. We audit what those engines say, correct inaccuracies at source, and monitor accuracy on an ongoing basis.
Ignited Nepal — Growth Engineering Company. Specialists in AI visibility for Nepali export businesses.