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AI VISIBILITY ENGINEERING
International buyers use ChatGPT, Perplexity, and Gemini to research Nepali suppliers before they make contact. If your entity footprint is missing, those buyers find someone else. We build the Wikipedia entries, Wikidata records, Knowledge Graph data, and directory citations that make AI engines recognise, trust, and cite your brand.
Your entity footprint is either working for you or against you in every AI answer generated about your sector.
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/ This is for you if
The invisible brand — Your website ranks well and your SEO is solid — but type your brand name into ChatGPT and you get silence or a vague, inaccurate summary. Your entity footprint does not exist in the databases AI engines actually use.
The searched founder — You searched your own company in ChatGPT. The response was thin, wrong, or missing entirely. That is not an SEO problem — it is an entity problem, and it requires a different fix.
The informed CMO — You know entity SEO matters. You have read about Knowledge Graphs and structured data. But no one on your team has ever systematically audited and built your entity footprint from the ground up.
The growing exporter — Your company has expanded significantly since you started — new markets, new products, new team. AI engines are still describing the 2019 version of your business, because your entity records were never updated to reflect the growth.
/ What's broken
Wikipedia is the single most influential signal for AI model recognition. When your brand has no Wikipedia article, AI engines have no authoritative reference point for who you are. Brands without Wikipedia are systematically underrepresented in AI-generated answers — including answers read by your international buyers.
Wikidata is the structured database that LLMs query for entity facts: founding date, headquarters, industry classification, identifiers. If your Wikidata record does not exist or has gaps, AI engines cannot confidently retrieve or cite your brand — even if they have heard of you.
Your company name appears differently across platforms. Your industry category changes depending on who listed you. Your description on one directory contradicts another. AI entity matching depends on consistent signals — inconsistency causes AI engines to lower their confidence score for your brand.
AI engines build knowledge from structured, authoritative sources — not just your website. If you are absent from the directories, trade associations, and citation sources that AI models have been trained on, your brand has a thin knowledge footprint regardless of how strong your SEO is.
/ What we engineer
Each workstream is built by the team that audits it, and wired into the rest of your growth system.
A full assessment of your current entity footprint: Wikipedia, Wikidata, Google Knowledge Graph, and 25 key directories. We document what exists, what is missing, and what contains inaccurate data — with priority scores for each gap.
We write or update your Wikipedia article to meet notability standards. For Nepali businesses targeting international buyers, this is the highest-leverage entity signal we build. A well-sourced, well-structured Wikipedia article tells AI engines who you are and gives them a citable reference.
We create or complete your structured Wikidata record — founding date, registered HQ, industry classification, identifiers (GLEIF, company register, social profiles). This is the machine-readable entity layer that LLMs query directly.
We claim and verify your Knowledge Panel, then ensure all data fields are accurate and complete. A verified Knowledge Panel raises AI confidence in your brand and ensures your entity data is pulled correctly into AI-generated answers.
Structured citations across 20+ authoritative sources — Crunchbase, industry trade associations, Nepal-specific business directories, export authority listings, and international B2B platforms. Each citation reinforces your entity footprint.
We standardise your brand name, description, industry category, and founding details across every source. Consistent entity signals eliminate AI confusion and raise the confidence score AI engines assign to your brand when generating answers.
/ What changes
/ Common questions
Straight answers on scope, timelines and cost. Anything else, ask us directly.
Ask us directlyEntity trust building is the process of creating and standardising the structured brand data that AI engines use to recognise and cite a company. It covers Wikipedia articles, Wikidata records, Google Knowledge Graph data, and directory citations — the sources LLMs query when generating answers that mention a brand.
AI engines rely on structured, authoritative data sources — not just your website. If your Wikipedia entry does not exist, your Wikidata record is incomplete, and your directory presence is thin, AI models have insufficient data to confidently cite you in generated answers.
Wikipedia is the single most influential entity signal for AI model recognition. Brands without a Wikipedia article are systematically underrepresented in AI-generated answers. It is possible to improve AI visibility without Wikipedia, but the impact is substantially lower.
The entity gap audit completes in the first five days. Core entity building — Wikipedia, Wikidata, and Knowledge Graph — takes six to eight weeks. Directory citation building runs through months one and two. Full entity footprint establishment typically takes two to three months.
Pricing depends on the scope of the gap audit findings and the number of entity assets that need to be created or corrected. Most Nepal-based engagements start from NPR 120,000 for a full entity build. Contact us at info@ignitednepal.com for a scoped proposal after your audit.
International buyers use English-language AI tools to research suppliers before making contact. If your brand has no entity footprint in the sources those AI tools query — Wikipedia, Wikidata, Crunchbase, industry directories — you do not appear in the research phase that precedes buyer outreach.
Wikidata is a structured, machine-readable database of entity facts maintained by the Wikimedia Foundation. It contains founding dates, headquarters locations, industry classifications, and unique identifiers for organisations. LLMs query Wikidata directly when generating factual statements about companies — an incomplete Wikidata record means missing or wrong AI citations.
Inaccurate AI descriptions almost always trace back to inaccurate or inconsistent entity data in the sources AI engines learn from. We audit those sources, correct the data, and standardise it — which causes AI engines to update the descriptions they generate about your brand over subsequent training cycles.
Start here
Right now, AI engines are generating answers about your sector, your product category, and your competitors. Whether your brand appears in those answers — and whether it is described accurately — depends on one thing: your entity footprint. We build it from the ground up. Wikipedia article, Wikidata record, Knowledge Panel, and 25+ authoritative directory citations — all standardised and consistent so AI engines can find you, recognise you, and cite you with confidence.
Ignited Nepal — Growth Engineering Company. Built for brands that need to be found by the AI engines their buyers already use.Ignited Nepal
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