AI Search Visibility
Entity Optimisation
Most brand entities are scattered across the web in inconsistent forms. Strategic entity work makes sure AI engines know who you are, what you do, and why you matter, cited as the canonical answer, not a maybe.
Deliverables
Entity audit
Documented review of how your brand currently appears across Wikidata, Wikipedia, AI engine knowledge graphs, and authoritative sources.
Entity graph design
A coherent design for how your organisation, products, people, and brand entities connect through structured data and external references.
Wikidata and authoritative source work
Wikidata entity creation or improvement, plus structured submissions to other authoritative entity databases where appropriate.
sameAs link infrastructure
Explicit linking between your schema markup and authoritative entity sources: Wikipedia, Wikidata, LinkedIn, Crunchbase, and the rest.
Entity-rich schema markup
Organization, Brand, Person, and Product schema written with full entity attributes and external references.
AI citation monitoring
Baseline and ongoing tracking of how AI engines describe your brand. Drift detection for when AI engines change their understanding over time.
Process
Discovery call
Thirty minutes. We walk through your brand, your products, your current entity presence across the web, and your goals for AI Search recognition. By the end I know what scope fits.
Entity audit
Two to three weeks. Document the current entity state: how your brand appears on Wikidata, Wikipedia, Crunchbase, LinkedIn, AI engine knowledge graphs, and across your own schema markup. Output is a documented audit with prioritised opportunities.
Entity graph design
I design the entity graph: how your organisation, brand, products, and people connect, what sameAs references they need, what authoritative sources they should link to. Every choice documented with reasoning.
Implementation
The actual work: Wikidata entity creation or improvement, schema markup updates with full entity attributes, sameAs link deployment across your site. Validated against schema standards and entity database submissions.
Monitoring baseline and handover
AI citation monitoring setup across ChatGPT, Perplexity, Claude, and Google AI Overviews. Drift detection so you know when AI engines update their understanding of your brand. Documented handover for your team.
Packages
Entity Audit
From £1,800
Current state of your brand across the web
2 to 3 weeks
- Review of current entity presence across Wikidata, knowledge graphs, and authoritative sources
- How AI engines currently describe your brand
- Entity graph gap analysis
- Confusion or disambiguation risk assessment
- Prioritised recommendations document
Full Implementation
From £5,200
Audit + entity work + schema deployed
6 to 8 weeks
- Everything in the audit
- Entity graph architecture designed
- Wikidata entity creation or improvement
- sameAs link infrastructure across schema markup
- Entity-rich Organization, Brand, Person, and Product schema
- AI citation monitoring baseline
Ongoing Monitoring
From £700/month
Citation tracking with drift detection
Monthly cadence
- Monthly citation tracking across ChatGPT, Perplexity, Claude, and Google AI Overviews
- Drift detection when AI engines change their understanding
- Quarterly entity record reviews and updates
- Wikidata refresh and authoritative source maintenance
- Direct email access for entity questions
Case Studies
Halewood Editorial
Disambiguation for a confused brand entity
Editorial brand was being confused with a different company sharing a similar name in AI responses. Built distinct entity records on Wikidata, restructured Organization schema with explicit sameAs links to authoritative sources, and established the editorial brand as the canonical entity for its space.
Outcome:
Cendric
Wikidata entity built from scratch
B2B SaaS had no Wikidata presence despite four years of operation and substantial press coverage. Built the Wikidata entity from scratch with full attributes, linked the schema markup to authoritative sources, and established the entity for AI Search recognition.
Outcome:
Aldernode
Coherent entity graph for organisation, brand, and products
E-commerce brand had separate entities for the company, the brand, and key products with no clear graph between them. Built a coherent entity architecture connecting Organization to Brand to individual Products, with proper sameAs links across the structure.
Outcome:
FAQs
What is entity optimisation and why does my brand need it?
Entity optimisation is the work of making sure AI engines, search engines, and knowledge graphs understand exactly who your brand is, what you do, and how you differ from similar names. It is the difference between AI engines citing you as the canonical answer for queries about your space, and AI engines guessing or citing a competitor.
Do you create Wikipedia pages?
No. Wikipedia has strict editorial standards and conflict-of-interest policies. I do not create or edit Wikipedia pages for clients. What I do is Wikidata work (a separate database with different rules that allows direct submissions) and other authoritative entity sources. A Wikipedia entry is great if you have organic editorial support; building one as a service is not what I offer.
How long does Wikidata entity work take?
Initial Wikidata entity creation: two to four weeks from submission to acceptance. Iterations and refinements continue afterwards. The timeline depends on Wikidata moderator review queues and how robust your supporting evidence is. We plan for this in the engagement scope.
Will entity work improve our AI Search citations?
Generally yes, with caveats. Strong entity work improves how AI engines understand and cite your brand. The trajectory is positive for sites that invest. What I do not guarantee is specific citation counts. We measure trajectory and recognition quality, not just absolute outcomes.
What is the difference between schema markup and entity work?
Schema markup is the structured data on your site. Entity work is the broader architecture: schema plus external entity records (Wikidata and others) plus authoritative sameAs links plus consistency across the web. Schema is a subset. Entity work is the full structure that makes schema authoritative.
Can you fix incorrect AI-generated descriptions of our brand?
Often yes, with caveats. If AI engines are describing your brand incorrectly, the cause is usually missing authoritative sources or inconsistent information across sources. We address both. The fix is rarely instant. AI engines update their understanding over weeks or months, not days.
How do you measure success?
Three layers. First, "structural": entity records exist on Wikidata or equivalent, schema markup contains proper sameAs references. Second, "recognition": AI engines and search engines surface your entity in knowledge panels and responses. Third, "drift": tracked descriptions of your brand across AI engines, with drift detection over time.
Do I keep the documentation and entity records?
Yes. Everything I deliver is yours: the audit document, the entity graph design, the deployed schema, the Wikidata entity records (which become public property by design anyway), the monitoring setup. Your team can run with it after the engagement closes.