Entity consistency is the practice of describing your company in matching language across every public surface that AI engines retrieve. Your homepage, G2 listing, LinkedIn profile, Crunchbase entry, Wikipedia article (if any), product directories, and review sites all feed the entity graph that ChatGPT, Claude, and Perplexity consult before they answer. When those surfaces disagree about who you are, the AI's confidence drops and your mention rate drops with it.
Why this matters more than you expect
Public benchmarks show that brand mentions across the web correlate roughly three times more strongly with AI visibility than backlinks do. The mechanism: the LLM resolves your brand name to an entity by triangulating across the public surfaces, and a coherent set of descriptions strengthens the resolution. If your homepage says you are a CRM, G2 lists you under Sales Engagement, and LinkedIn calls you a customer success platform, the model has to pick one — and may pick none.
The three lines that have to match
Most entity drift fixes itself once three things are aligned across all surfaces. First, the one-sentence company description: a single canonical sentence that names what you do, for whom, and the differentiator. Second, the category placement: pick the single primary category every directory should slot you under. Third, the headline competitors and integrations: the named entities that should appear in any comparison about you. Once these three are consistent across surfaces, the entity graph reliably resolves to you.
Where to fix drift first
Start with G2 and LinkedIn — they have the highest weight in most B2B entity graphs. Then Capterra and TrustRadius. Then Crunchbase and Wikipedia (if the page exists). Then product-specific directories like Product Hunt, the Stripe App Marketplace, the HubSpot Marketplace, the Shopify App Store, and category-specific lists (Awesome lists on GitHub for developer tools, for example). Each surface takes 30 minutes to update and the cumulative effect on AI mention rate is large.
What to align beyond text
Logos, founding date, headquarters location, founder names, and pricing tier names should also be consistent. AI engines pull these into structured fields when they exist. Mismatched founder names or founding dates between Crunchbase and Wikipedia is enough to trigger an LLM to either hedge or omit the brand. Pick the canonical version of each and align the rest to it.
How to spot drift over time
Set a quarterly check on the four highest-weight surfaces. Drift creeps in as different team members update different surfaces over time, especially after a positioning change. The fix is fast — text edits in directory profiles — but it has to actually happen. AskRanker's gap analysis flags entity drift automatically when it detects that AI answers describe you differently than your homepage does, and the Execute playbook lists the specific surfaces to update.