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ENTITY & SCHEMA AUDIT
AI engines — ChatGPT, Gemini, Perplexity, Claude — do not search the web the way Google does. They reason from structured knowledge: entity records, verified data points, and schema markup that tells them exactly what a business is, what it does, and whether it can be trusted. If your entity data is incomplete or your schema markup is broken, you are invisible to that reasoning layer regardless of how well your website ranks in traditional search. Ignited Nepal's Entity and Schema Audit maps your business across 25 authoritative data sources, crawls every schema markup element on your site, validates each one against current Schema.org and Google requirements, and produces a priority-ranked fix plan built for AI visibility. This is a technical audit, not a content review — it goes where most SEO work never reaches.
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/ This is for you if
Established UK businesses being overlooked by AI engines. You have a real trading history, a Companies House record, industry body memberships, press coverage — yet AI assistants either ignore you or describe you inaccurately. The entity data that should establish your credibility is fragmented, incomplete, or contradictory across sources.
Professional services firms in regulated sectors. Solicitors, accountants, chartered surveyors, financial advisers, and healthcare providers operate in sectors where authoritative directory listings (Law Society, ICAEW, FCA Register, NHS profiles) carry significant weight with AI engines. If those records are thin or disconnected from your web presence, AI tools cannot establish the authority chain they need to cite you.
Multi-location UK retailers and hospitality businesses. Every location introduces fresh opportunities for NAP inconsistency, schema conflict, and duplicate entity records. A business with fifteen locations may have fifteen slightly different versions of its own entity data drifting across the knowledge graph — each one eroding AI confidence in the others.
B2B technology and professional services companies targeting enterprise buyers. Procurement teams at large organisations increasingly use AI research tools before shortlisting suppliers. If your business entity is poorly defined in the knowledge graph, you are filtered out before a human ever evaluates you.
/ What's broken
Companies House has one registered address. Your Google Business Profile has another. Your Crunchbase entry has an outdated description from 2019. Yell.com has a phone number that changed three years ago. Each inconsistency reduces the confidence score AI engines assign to your entity — and when confidence falls below a threshold, AI tools simply stop citing you.
Many UK businesses have schema markup added at some point by a developer or an SEO plugin — but it was never properly validated, never updated as Schema.org evolved, and never tested against Google's structured data requirements. Invalid markup is not neutral: it actively confuses the data layer that AI engines read first.
Wikipedia and Wikidata are primary reference sources for every major AI engine's knowledge graph. A business without a Wikipedia article or Wikidata entry is missing from the canonical source that AI engines treat as ground truth. A business with an outdated, stub, or uncited Wikipedia entry may be worse off than one with none at all.
The Law Society Solicitors Register, ICAEW firm directory, FCA Register, NHS Choices, and major UK trade association directories are high-authority entity data sources that AI engines specifically reference when evaluating UK professional services businesses. Gaps or mismatches in these sources create authority breaks that no amount of on-page content can repair.
/ What we engineer
Each workstream is built by the team that audits it, and wired into the rest of your growth system.
a structured inventory of your business entity across Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Companies House, Crunchbase, LinkedIn, Yell.com, ICAEW directory, Law Society register, FCA Register, NHS profiles (where applicable), Bing Places, Apple Maps, industry trade association directories, news archives, press release databases, academic citation sources, regional business directories, and additional sector-specific sources identified during scoping.
each source scored on completeness (are all key fields populated), accuracy (do the data points match verified facts), and consistency (does this source agree with all others). Every contradiction and gap is flagged with a severity rating.
extraction of every schema markup block present on your domain, covering Organisation, LocalBusiness, Product, Article, FAQ, HowTo, BreadcrumbList, Service, Person, and any custom types present.
each markup block tested against Schema.org specification and Google's structured data requirements, with errors ranked by AI visibility impact from critical to advisory.
your entity coverage and schema quality compared against three direct competitors, showing precisely where gaps exist relative to businesses already being cited by AI engines.
a sequenced action list combining entity gap remediation and schema error resolution, ranked by expected AI visibility impact rather than technical complexity.
/ What changes
/ Common questions
Straight answers on scope, timelines and cost. Anything else, ask us directly.
Ask us directlyA business entity is the structured data record that AI engines use to understand what your organisation is, what it does, where it operates, and whether it is credible. It is distinct from your website content — it exists across Wikipedia, Wikidata, Google's Knowledge Graph, business directories, and dozens of other data sources that AI systems consult when deciding whether to include your business in a generated answer.
Schema markup is structured code embedded in your website that describes your content in a format machines can read without interpretation. AI engines and search systems use schema markup to understand the precise nature of your products, services, organisation structure, and content without needing to infer meaning from prose. Absent or invalid schema markup forces AI systems to guess — and they frequently guess wrong, or decline to cite you at all.
The audit covers: Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Companies House, Crunchbase, LinkedIn, Yell.com, Bing Places, Apple Maps, ICAEW directory, Law Society register, FCA Register, NHS Choices (where applicable), industry trade association directories, regional chamber of commerce listings, press archive databases (Factiva, Nexis), PR Newswire, Business Wire, Acxiom data feeds, Neustar data feeds, academic citation databases, and sector-specific directories identified during onboarding. The exact mix is adjusted based on your industry and the sources most relevant to your entity type.
The standard audit takes ten to fourteen working days from the date we receive your onboarding information. If your site has a very large number of URLs or your industry requires a broader directory review, we will confirm a revised timeline before starting.
Pricing for the Entity and Schema Audit starts at £1,800 for single-location businesses and increases based on the number of locations, the breadth of industry-specific directories in scope, and whether you require the competitive benchmark component. A precise quote is provided after a brief scoping call.
The priority fix plan is designed to be actionable by your internal team or developer with no further input from us. For clients who prefer a managed implementation, Ignited Nepal offers a separate Entity and Schema Implementation service that carries out every item in the fix plan and re-validates the results. We also offer a monthly monitoring retainer that tracks entity data drift and schema changes over time.
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AI engines have already formed an opinion of your business based on whatever entity data they found. The audit tells you what that data says, where it breaks down, and precisely what to fix. The longer incomplete or contradictory entity records sit uncorrected, the more AI visibility ground you cede to competitors who have done this work.
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