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Google's Knowledge Graph, ChatGPT, Perplexity, and Gemini don't browse your website to learn who you are. They read structured entity data — Wikipedia articles, Wikidata records, Knowledge Graph entries, and verified directory citations. If that data doesn't exist or contradicts itself, AI engines either skip your brand entirely or describe you inaccurately to the people actively searching for what you offer. Ignited Nepal builds and verifies the entity infrastructure that tells AI engines — and search algorithms — exactly who your organisation is, what it does, and why it deserves to appear in authoritative answers.
Entity Trust Building is designed for UK businesses and organisations that have outgrown the visibility their current digital footprint provides.
Professional services firms — solicitors, accountancy practices, management consultancies, and financial advisers regulated by the FCA, ICAEW, or the Law Society whose clients search for credentialed experts and need to find the right firm, not a generic result.
Mid-market and enterprise B2B companies — manufacturers, technology providers, logistics operators, and specialist service businesses with revenues between £5 million and £500 million that are increasingly being evaluated by AI-assisted procurement research before a human ever picks up the phone.
Healthcare and life sciences organisations — NHS suppliers, CQC-registered providers, medical device companies, and pharmaceutical businesses where accurate entity data directly affects whether AI systems recommend or surface them in clinical and commissioning research.
Scale-up founders and executive teams — companies that have built genuine market credibility over years of trading but whose digital entity footprint is thin, inconsistent, or simply absent, leaving them invisible in the AI-generated answers their prospects are reading right now.
When a procurement manager asks ChatGPT or Perplexity to identify the leading providers in your sector, the brands that appear are not necessarily the best — they are the ones with the most coherent entity data. If your Wikipedia article doesn't exist, your Wikidata record is empty, and your Knowledge Panel is unclaimed, the AI has nothing structured to work with and defaults to whoever is best documented.
Partial entity data is frequently worse than no entity data. AI engines trained on incomplete records can confidently state incorrect founding dates, wrong descriptions, outdated leadership, or mistaken service categories. Correcting an impression that has already been formed in a prospect's mind is substantially harder than building the correct record from the start.
Your Companies House registration, your Yell listing, your LinkedIn page, and your website all describe your business slightly differently — different trading names, different address formats, different founding year references. To a Knowledge Graph algorithm, these inconsistencies suggest multiple entities or an unverified one, and the result is suppressed visibility across every platform that draws on structured data.
AI systems are trained on and continue to cite a defined set of authoritative sources: Wikipedia, Wikidata, Companies House, industry-specific registries, and major directories. If your brand does not appear in those sources with clean, consistent, cross-referenced data, no amount of website content or social media activity will substitute for the entity signals those sources provide.
Ignited Nepal's Entity Trust Building service constructs the structured data foundation that makes your brand recognisable, credible, and accurately represented across the sources that AI engines and search algorithms consult. We begin with a rigorous audit of your current entity coverage — not just a surface check, but a thorough examination of what Wikipedia says (or doesn't say) about your organisation, what your Wikidata record contains, whether your Google Knowledge Panel is claimed and accurate, and how your entity data is represented across the directories that matter most in your sector and region. From that audit, we design an entity architecture that is internally consistent and optimised for machine readability. Then we build it.
Written to Wikipedia's notability and neutrality standards for UK organisations, with proper citations to Companies House filings, press coverage, and industry sources.
With structured attributes: company identifiers, industry classifications, founding date, registered address, key personnel, official website, Companies House number, and relationship links to parent and subsidiary entities.
Including correct categorisation, verified contact data, and attribute completeness.
Including Companies House, Yell, Thomson Local, ICAEW member directories (where applicable), Law Society directories (where applicable), NHS supplier registers (where applicable), Bing Places, Apple Maps, and sector-specific trade body listings.
To ensure name, address, phone number, and description are uniform across every citation.
Delivered as a structured asset you own, with verification links and ongoing maintenance guidance.
A brand entity is a structured, machine-readable record that defines your organisation as a distinct, identifiable subject — not just a collection of web pages, but a recognised thing with verified attributes, relationships, and identifiers that AI engines and search algorithms can reference with confidence. Entity records live in places like Wikipedia, Wikidata, and Google's Knowledge Graph, and they function as the authoritative source of truth that AI systems consult when constructing answers, generating summaries, or populating knowledge panels. If your entity record is absent, incomplete, or inconsistent, AI engines either omit your brand from relevant answers or represent it inaccurately — neither of which serves your commercial interests.
No. Wikipedia applies a notability standard that requires significant coverage in reliable, independent sources — established trade publications, national press, regulatory filings reported on by credible outlets, or recognised industry databases. Companies with limited third-party press coverage may not currently qualify for a standalone Wikipedia article. In those cases, we identify the path to qualification: what coverage needs to exist, which sources carry sufficient weight, and whether a Wikidata record and Knowledge Graph entry can achieve the entity signalling goals without a full Wikipedia article. We will tell you clearly in the Entity Gap Audit what is achievable and what the preconditions are for what is not yet achievable.
The Entity Gap Audit and Entity Structure Plan are typically completed within two weeks. The Core Entity Build — Wikipedia article, Wikidata enrichment, and Knowledge Graph submission — takes a further three to five weeks, depending on the complexity of your organisation's history and the state of your existing citations. The Directory Citation Campaign runs in parallel with the entity build and is typically complete within four weeks. From engagement to a fully deployed entity architecture, most UK clients should plan for eight to twelve weeks. Wikipedia article review timelines can vary as they depend on volunteer editorial processes, but we manage that process throughout.
Entity Trust Building for UK organisations is priced from £3,500 for the full programme, covering the audit, entity architecture design, Wikipedia and Wikidata work, Knowledge Graph submission, 20+ directory citations, and the first quarterly monitoring review. Organisations with more complex entity structures — multiple trading entities, regulated professional body memberships, or subsidiary relationships — are priced on the basis of the audit findings. All pricing is confirmed in writing before any work begins.
The directories that carry the most weight for UK entity trust are those that AI engines and search algorithms treat as authoritative reference sources. Companies House is the foundational record — every other citation should be consistent with your registered data there. For general business visibility, Yell and Thomson Local remain significant. For professional services, the relevant body directory (ICAEW, Law Society, RICS, FCA register) carries substantial weight because AI engines recognise these as credentialed, verified sources. For healthcare and public sector suppliers, NHS supplier registers and G-Cloud listings carry specific weight. We select the 20+ citations based on your sector and the sources that matter for your specific entity type.
AI engines — including the large language models powering ChatGPT, Perplexity, Gemini, and Bing Copilot — are trained on and continue to reference structured, authoritative data sources. When those systems encounter a query about your sector or brand, they check whether they have a coherent, verified entity record to draw on. A well-constructed entity record that exists consistently across Wikipedia, Wikidata, Google Knowledge Graph, and major directories gives AI systems the confidence to include your brand in answers, attribute correct information to you, and represent your organisation accurately in AI-generated summaries. It is the structural foundation beneath every other form of AI visibility — without it, content marketing, link building, and PR all operate at a fraction of their potential reach.
Entity records take months to mature, Knowledge Panels take time to stabilise, and Wikipedia articles require a track record of credible coverage. Every month you delay is another month your competitors' entity signals compound while yours remain thin. Start with a clear picture of where your entity infrastructure stands.
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