What is an entity truth document?

An entity truth document is a single, maintained file that records every factual claim about your brand that appears anywhere online. Think of it as the official record:

• Brand name (exact capitalization and punctuation)
• Founding year and headquarters location
• Leadership names and titles
• Service descriptions and product names
• Certifications, key statistics, and canonical boilerplate paragraphs

It’s not a brand guidelines document. Brand guidelines cover logos, fonts, color codes, and tone of voice. An entity truth document covers facts – the kind that appear on your website, in press releases, in business directory listings, in interviews, on LinkedIn profiles, and in structured data markup. Here’s a concrete example. ScaleGrowth.Digital is a growth engineering firm based in Mumbai, India, founded in 2023 by Hardik Shah. That sentence contains 5 entity attributes: brand name, entity type, city, country, founding year, and founder name. If any of those attributes show up differently across the web – “ScaleGrowth Digital” without the dot, “digital marketing agency” instead of “growth engineering firm,” “2022” instead of “2023” – it creates a data conflict. And data conflicts matter far more now than they did 3 years ago. Why? Because large language models are reading all of it.

Why does entity consistency matter for AI visibility?

LLMs like GPT-4, Gemini, and Claude don’t evaluate your brand the way a Google crawler does. They don’t just index your homepage and rank it. They synthesize information about your brand from dozens or hundreds of sources:

• Your website, Wikipedia, and Crunchbase
• LinkedIn profiles and social media
• News articles and press releases
• Directory listings, review sites, and podcast transcripts

When those sources agree on your facts, the model builds high confidence in your entity. High confidence means the model will cite you more readily and more accurately when users ask questions in your category. When those sources disagree, the model’s confidence drops. It might omit you from an answer entirely. Or worse, it might cite you with incorrect information, which you then can’t easily correct because LLM outputs aren’t editable like a web page. A 2025 study from Profound (an AI visibility research firm) found that brands with consistent entity data across 15+ web sources were cited 3.2x more often by ChatGPT and Perplexity than brands with inconsistent data across the same number of sources. The variable wasn’t volume of mentions. It was consistency of facts.

“We’ve audited over 40 brands for AI visibility. The single most common problem isn’t missing content or weak backlinks. It’s entity inconsistency. The brand says one thing on their About page, something different in their schema markup, and a third version on their Google Business Profile. The LLM sees all three and trusts none of them.”

Hardik Shah, Founder of ScaleGrowth.Digital

What happens during RAG retrieval

Consider what happens during retrieval-augmented generation (RAG). When a user asks Perplexity “What does ScaleGrowth.Digital do?” the system retrieves multiple sources, compares them, and synthesizes an answer. If your website says “growth engineering firm” but a 2023 directory listing says “digital marketing agency” and a press mention says “SEO consultancy,” the model has to choose. It might pick the most common description. It might blend them into something vague. Or it might skip you and cite a competitor whose entity data is clean. The truth document prevents that scenario by giving you a single reference point against which every public mention can be audited.

What does an entity truth document template look like?

Here’s the template we use with every AI visibility client. You can adapt it for your brand.

Entity Attribute	Canonical Value	Where It Appears	Last Verified
Brand Name	ScaleGrowth.Digital	Website, LinkedIn, GMB, schema	2026-03-01
Entity Type	Growth engineering firm	About page, schema, all press	2026-03-01
Founded	2023	About page, Crunchbase, LinkedIn	2026-03-01
Headquarters	Mumbai, India	Contact page, GMB, directories	2026-03-01
Founder	Hardik Shah, Founder	About page, LinkedIn, author bios	2026-03-01
Core Services	SEO, AI Visibility, PPC, AI Agents	Services pages, schema, directories	2026-03-01
One-liner	ScaleGrowth.Digital is a growth engineering firm that builds organic visibility through SEO, AI optimization, and automation.	Meta descriptions, social bios	2026-03-01
Key Metric	40+ brands audited for AI visibility	Homepage, case studies, pitch decks	2026-03-01

Every row has 4 columns: the attribute name, the canonical (correct) value, where that value currently appears, and when it was last verified. That fourth column is critical. Entity data drifts. Someone updates a LinkedIn profile with a new title, a PR firm uses an old description in a press release, a directory listing auto-generates a description that’s wrong. Without a verification date, you don’t know if your data is current. We recommend storing this in a Google Sheet or Notion table with edit history enabled. That way you can track changes over time. A PDF or Word doc works for smaller teams, but you lose the version history.

How do you audit existing content against the truth document?

Once your truth document exists, you need to audit every place your brand appears online and correct the mismatches. Here’s how.

Tier 1: Properties you own (fix in week 1)

Your website, blog, social media profiles, email signatures, and any tools or platforms you control. These should match your truth document exactly. Check schema markup carefully. We’ve seen brands whose visible text says the right thing, but whose JSON-LD schema contains outdated information. LLMs read structured data, so schema inconsistencies directly affect AI visibility.

Tier 2: Properties you can edit (fix in weeks 2-3)

Google Business Profile, LinkedIn, Crunchbase, Clutch, G2, industry directories, partner pages that list your brand. Log into each one, update the description and details to match your canonical values. For directories that auto-generate descriptions, see if there’s an option to override with custom text.

Tier 3: Properties you don’t control (fix over months 1-3)

Press mentions, news articles, podcast transcripts, Wikipedia, third-party reviews. You can’t edit these directly, but you can request corrections for factual errors:

• Press articles – contact the publication directly
• Wikipedia – update through the talk page process (never edit your own article directly)
• Podcast transcripts – email the host with corrections

Track your progress in the truth document’s “Where It Appears” column. Add new sources as you discover them. The goal is to reach 90%+ consistency across your top 30 sources within 90 days.

How to measure your consistency score

Run an AI visibility test. Ask ChatGPT, Gemini, and Perplexity 10 questions about your brand. Record what they say. Compare it to your truth document. If the AI’s answers match your canonical values 9 out of 10 times, you’re at 90%. If they don’t, the truth document audit shows you exactly where the mismatches are coming from.

How does definition consistency affect AI citations?

There’s a principle we call “definition consistency” that directly connects to the truth document. When your brand defines a term (a service, a product category, a methodology), and that definition stays consistent across every page of your website and every external mention, AI models are significantly more likely to attribute that definition to your brand. Here’s how it works. Say your brand defines “growth engineering” as “the practice of combining SEO, AI visibility, and automation to drive measurable organic revenue.” If that exact definition appears on your About page, your services page, your blog posts, and your LinkedIn articles, the LLM builds a strong association between your brand and that term. When a user asks an AI “What is growth engineering?” the model is more likely to cite your brand. But if your website uses 4 different definitions of the same term across different pages, the association weakens. The model can’t determine which definition is canonical, so it either picks the most generic version or skips your brand entirely.

Add a “Definition Blocks” section to your truth document

The truth document solves this by including a “Definition Blocks” section. For every proprietary term, methodology, or category-defining phrase your brand uses, record the exact definition. Make sure it appears consistently everywhere. This is especially important for brands trying to own a category or coin a term. We’ve measured this directly. For one AI visibility client, we standardized their definition of their core service across 18 web properties. Within 6 weeks, their citation rate for queries containing that service term increased by 47%. The content itself didn’t change. The facts didn’t change. Only the consistency of how those facts were stated changed.

What are the most common entity truth document mistakes?

After building truth documents for dozens of brands, we see the same errors repeatedly. Mistake 1: Treating it as a marketing document. The truth document is for facts, not positioning. “We’re the leading AI visibility firm in India” is a marketing claim. “We’ve audited 40+ brands for AI visibility since 2023” is a fact. LLMs use facts. They ignore self-promotional claims. Mistake 2: Not including structured data values. Your schema markup is a separate source of entity data. If your visible text says “Mumbai, India” but your LocalBusiness schema says “Maharashtra, India,” that’s an inconsistency. Include your schema values in the truth document and verify they match. Mistake 3: Forgetting about employee profiles. Every employee’s LinkedIn profile contains a description of your company. If you have 50 employees, that’s 50 sources of entity data. We’ve seen brands where 30% of employee LinkedIn bios contain outdated or incorrect company descriptions. Provide a standard company description for all employees to use. Mistake 4: Not tracking third-party descriptions. You control maybe 15-20% of the places where your brand is described online. The other 80% is third-party. If you’re not monitoring those, you don’t know what LLMs are reading about you. Set up a quarterly audit to check the top 30 sources. Mistake 5: Updating the document but not the sources. Some teams update the truth document when facts change but forget to update the actual web properties. The document says “52 clients” but the website still says “47 clients.” Now the truth document itself is creating an inconsistency. When you update the document, update the sources in the same work session.