Become a first-class entity in the AI knowledge graph
Entity Optimization so AI engines understand exactly who you are.
LLMs reason in entities, not strings. We resolve your brand, products, people and concepts into unambiguous entities across the open web, structured data and knowledge graphs.
The problem
Ambiguous entities lose the answer
If a model can't disambiguate your company from a near-namesake, can't connect your product to its category, or can't link your experts to their claims, you lose the citation by default.
- Brand entity confusion across Wikipedia, Wikidata and search graphs
- Product entities not linked to canonical categories
- Expert authors lacking E-E-A-T entity scaffolding
- Inconsistent NAP and structured data across the footprint
The solution
End-to-end entity resolution
We engineer the entity graph that AI engines rely on, from your website to the public knowledge bases.
Brand entity hardening
Resolve your brand across Wikipedia, Wikidata, search knowledge panels and AI memories.
Product & concept graphs
Structured ontologies that connect what you sell to how it's described in the wild.
Expert entity scaffolding
Build author and SME entities with verifiable expertise signals.
Structured data layer
Schema.org + custom JSON-LD covering every entity surface on the site.
The process
How we run the program.
- 01
Entity audit
Map current entity footprint across web, knowledge graphs and AI engine memory.
- 02
Resolution plan
Prioritize disambiguation, enrichment and creation work by visibility impact.
- 03
Implementation
Wikipedia, Wikidata, schema, knowledge panel and on-site entity work.
- 04
Reinforcement
Citations and authoritative mentions to lock in the resolved entity.
- 05
Monitoring
Continuous entity drift monitoring across surfaces.
Benefits
What you walk away with.
- AI engines describe your brand the way you actually describe it
- Higher citation precision and lower hallucination risk
- Experts become recognizable authority sources
- Foundation for every other AI visibility investment
Deliverables
In the engagement.
- Entity audit & graph map
- Wikipedia / Wikidata strategy
- Knowledge panel optimization
- Structured data rollout
- Author / expert entity build-out
Case study
Quanton Finance
Fintech
Challenge
Persistent brand entity confusion with a similarly-named retail bank caused recommendation leakage in AI answers.
Result
Disambiguated the entity across knowledge bases. Hallucinated competitor mentions dropped to zero in a quarter.
31 → 0
Hallucinated mentions
+92%
Knowledge panel coverage
44% → 96%
Correct citation rate
FAQ
Questions we get on ENT.
Can you really edit Wikipedia?
We don't astroturf. We work through notability and reliable sourcing — building the third-party signal that makes legitimate entries possible and survivable.
How does this affect classical SEO?
Entity optimization is increasingly load-bearing for Google rankings. The structured data and authority work compound on both surfaces.
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Learn moreReady to engineer your ENT program?
A 30-minute call with our team is the fastest way to size the opportunity and the plan.