What is an entity in the context of AI engine performance?
An entity is a uniquely identified concept — your business — that AI engines use as a structured reference point rather than treating your brand name as an unresolved text string. Google's Knowledge Graph, Wikidata, and similar knowledge bases store structured facts about entities: official name, industry classification, founding date, headquarters location, key people, related organisations, and more. When AI engines process queries about businesses in your category, they resolve brand references to known entities and draw on all associated structured data to form their responses. A business with a strong, consistent, and complete entity record is cited more confidently and more frequently than one with thin or contradictory entity data.
What is schema markup and why does it matter beyond SEO?
Schema markup is structured data — typically JSON-LD — embedded in your website's HTML that describes each page's contents using a standardised vocabulary from Schema.org. It tells AI crawlers and search engines explicitly whether a page contains a product, a service, an article, a FAQ, a business, or other content types. While schema markup is well known for its role in rich results in traditional search, its relevance to AI engine performance is equally significant: AI engines use schema markup alongside entity data to form a more accurate and complete understanding of your website. Invalid or missing schema forces AI engines to infer page meaning from unstructured text, which is both less accurate and less comprehensive.
What 25 sources does the entity coverage map check?
The 25 sources include Wikipedia, Wikidata, Google Knowledge Graph, Google Business Profile, Crunchbase, LinkedIn Company Pages, Bloomberg company profiles, SEC EDGAR (for applicable companies), D&B and Hoovers business registries, industry association member directories specific to your sector, US national and regional news archives, Apple Maps, Bing Places, Yelp, Facebook Business Pages, YouTube channel data for media brands, review platforms relevant to your industry, and additional structured databases AI engines are known to use for entity resolution. The exact list is adapted to your specific business category, size, and entity complexity.
How long does the audit take?
The Entity & Schema Audit is completed within 7 to 10 business days from receipt of your brand information brief. Delivery includes the full 25-source coverage map, schema crawl report, validation report, error severity ranking, entity gap analysis, and priority fix roadmap as a single packaged deliverable. For large enterprise websites with multiple subdomains or businesses with complex multi-entity structures, we advise on any timeline adjustments before starting.
What does the audit cost?
The Entity & Schema Audit is priced from USD 1,100 for a standard US business. Final pricing depends on website size, entity footprint complexity, and whether multi-brand or multi-entity coverage is required. We provide a fixed-fee quote after a brief scoping call. There are no ongoing fees — this is a one-time diagnostic engagement with a clean, fixed scope.
What happens after the audit is delivered?
After delivery you have three paths. You can implement the priority fix roadmap using your own SEO, PR, and development teams, working from the detailed correction instructions in the report. You can engage Ignited Nepal to implement the fixes as a separate project, executing the roadmap in priority order. Or you can use the audit as the entry point for a broader AI visibility programme — feeding the findings into a GEO content strategy, entity authority building, or structured brand answer accuracy work. The audit is designed to be immediately actionable as a standalone deliverable, and it integrates directly with every other AI visibility service Ignited Nepal provides.
