It is not a schema-only exercise. The source facts must first be true, consistent, accessible, and supported.
Build an entity fact ledger
Record the facts that answer systems and customers need:
- canonical name and approved aliases;
- entity type and category;
- parent, subsidiary, and product relationships;
- target audiences and supported use cases;
- founders, leaders, locations, and dates where material;
- product capabilities and boundaries;
- official domains, profiles, and documentation;
- claims, supporting evidence, and verification dates.
Assign an owner and source of truth to each field.
Find ambiguity and conflicts
Search official pages, documentation, profiles, directories, reviews, and high-value cited sources for:
- legacy product names presented as current;
- conflicting founding or leadership facts;
- unclear parent-brand relationships;
- unsupported superlatives;
- different descriptions of the same capability;
- duplicate or competing canonical pages;
- acronyms that match unrelated entities.
Resolve upstream facts before patching every downstream page.
Express the entity clearly
Use direct visible copy to state what the entity is, whom it serves, and how it relates to other products or services. Then add compatible structured data for supported facts. Keep visible content and markup aligned.
Measure description accuracy
A mention alone is not enough. Review Answer Snapshots for:
- correct entity resolution;
- current product description;
- accurate audience and use-case fit;
- unsupported or outdated claims;
- Sentiment;
- sources associated with the description.
Track inaccurate descriptions as a distinct issue from brand absence.
Entity optimization with UnderAI
UnderAI’s AI Visibility Audit builds a reviewed baseline of brand aliases, competitor relationships, answer descriptions, Sentiment, and Citation sources. Website AI SEO maps conflicts to the correct official owner. UnderAI GEO Workspace then monitors the relevant prompts and saved answers over time.
Request an entity and visibility audit or learn what AI visibility means.
Worked entity ledger for UnderAI
Entity optimization begins with verifiable facts, not schema markup alone. A minimum UnderAI ledger should distinguish:
| Fact type | Example owner | Publication rule |
|---|---|---|
| Organization name and domain | Homepage and About page | Use one canonical name and official URL |
| Product name | UnderAI GEO Workspace page | Keep product and company names distinct |
| Product role | Workspace page and documentation | Describe AI Performance Tracking and evidence workflow precisely |
| Supported platform scope | Product page and project documentation | Date the claim and distinguish defaults from configured availability |
| Metrics | Metrics guide and product UI | Publish formulas and denominators; separate Sentiment from accuracy |
| Commercial access | Product or pricing page | Do not invent list price, caps, export, or API terms |
UnderAI’s July 2026 baseline provides a measurable entity starting point, but brand absence alone does not prove an entity problem. Future appearances must still be reviewed for identity, role, factual accuracy, and source against the fact ledger. The full baseline belongs in the Citation benchmark.
Entity and structured-data sources
Google says Organization structured data on a homepage or organization page can help it understand administrative details and disambiguate an organization. It should contain applicable real-world and online facts, not speculative properties. See Google Organization structured data.
The structured data must match visible page content. Validate it after deployment, but treat the ledger and consistent public facts as the source of truth.
Four entity errors to test explicitly
Company and product conflation
An answer may use “UnderAI” for both the organization and GEO Workspace product. Review whether the role remains clear when the user asks about the company, software, or managed service.
Unsupported platform scope
An answer may repeat a broad “all major AI platforms” claim. Compare it with the dated product fact ledger and configured project scope; correct the official owner before promoting the claim elsewhere.
Metric confusion
An answer may treat Sentiment as description accuracy or call TOP3 a conventional search rank. Publish separate metric definitions and preserve their denominators.
Same-name or phrase collision
Strict matching excludes the generic phrase “under AI.” Review capitalization, domain, surrounding product language, and answer role before counting a brand mention.
Each error becomes a controlled test in the Prompt Panel. A corrected page is accepted only after its visible facts, structured data, internal anchors, and measurement rules agree.
Frequently asked questions
Is entity optimization the same as adding Organization schema?
No. Schema is one expression layer. Entity optimization begins with correct facts, clear relationships, consistent pages, and credible corroboration.
Should every external profile use identical copy?
The core facts should be consistent, but the presentation can fit the source and audience. Avoid contradictions and unsupported claims.
How do I measure improvement?
Retest fixed prompts and review entity resolution, description accuracy, Sentiment, competitor roles, and Citation sources across comparable answers.