We ran the same prompt — "what is [client brand]?" — across ChatGPT, Claude, and Perplexity for 40 different SaaS brands. Roughly a third returned descriptions that were either wrong or about a different company with a similar name. None of these were obscure brands. The problem isn't scale. It's entity ambiguity.

What it looks like

Three common failure modes we saw:

  • Wrong category.Brand name "Volley" — ChatGPT confidently described it as a podcast game show producer. The actual client was a B2B sales tool. Both companies exist; one is more cited in the model's training data; the model has no idea about the other.
  • Merged entity.Brand name "Atlas" — Claude returned a description that was half about the SaaS brand we were asking about and half about a completely separate mapping company. The model blended them into a chimera.
  • Stale description.Brand name "Lockstep" — Perplexity returned the company's 2022 positioning before they pivoted. The new positioning was on their homepage but not well-represented in external sources the AI engine retrieved.

Why it happens

AI engines model brands as entities (think Wikipedia entries), not as websites. The entity is built from many signals:

  • The brand's own owned media (homepage, About page, schema)
  • External mentions in trusted secondary sources (TechCrunch, G2, Crunchbase)
  • Wikidata and Wikipedia entries
  • The patterns the entity appears in across the broader web

When the brand name is unique (Stripe, Notion, Figma), all those signals converge on the same entity. When the brand name overlaps with another entity, the model has to pick. It picks the one with more cited support, usually older, often wrong for your brand.

How to fix it

Entity disambiguation is the term of art. The work is mostly about making your brand the obvious answer when someone (human or model) asks "which X are we talking about?"

Step 1: Audit what AI engines currently say

Run these prompts against ChatGPT, Claude, and Perplexity for your brand:

  • "What is [brand]?"
  • "Who founded [brand]?"
  • "When was [brand] founded?"
  • "What does [brand] do?"
  • "Where is [brand] headquartered?"
  • "Is [brand] the same as [confusion candidate]?"

Record each answer. Note any factual errors. These are your targets.

Step 2: Fix your owned signals first

  • One canonical descriptionused identically on your homepage, About page, schema markup, llms.txt, LinkedIn, Twitter bio, and Crunchbase. Same words. Don't paraphrase per channel.
  • Disambiguating phrasein your homepage's first paragraph. "[Brand], the [category] for [audience] — not to be confused with [common confusion]" if needed.
  • Schema with sameAs links to every external property you own (LinkedIn, Twitter, Crunchbase, GitHub, Wikipedia if you have an entry).
  • llms.txt with citation guidance: explicit one-line description, founding date, founder, headquarters. See our llms.txt guide.

Step 3: Earn external entity confirmation

AI engines weight external mentions heavily. Slow but compounding:

  • Wikidata entry.If you can establish notability (press coverage, funding announcements, etc.), get a Wikidata entry. It's machine-readable, free, and explicitly retrieved by AI engines.
  • Trusted-source mentions. Press in trade publications, podcast appearances, conference talks. Each one is an external signal confirming your entity.
  • Consistent founder/team biographies. When founders appear in profiles, podcast notes, conference bios, make sure they consistently mention the company in the same way.
  • Press releases at milestones. Funding rounds, product launches, partnerships. These get indexed widely and give models recent dated evidence of your entity.

Step 4: Re-test in 30 and 60 days

Entity changes propagate slowly. AI engines don't re-crawl your homepage every day, and even when they do, the entity model update isn't live. Plan for 30-60 days between changes and measurable improvements in the prompt audit.

When to consider renaming

Honest section. If your brand name overlaps with a much larger entity (a publicly traded company, a Wikipedia-tier institution, a famous person), entity disambiguation may not work. Cost-benefit: if you're early-stage, renaming might be cheaper than fighting the ambiguity forever. We've had this conversation with two clients in the past year. Both renamed.

Most companies don't need to. The disambiguation work above is enough. But it's worth knowing the option exists.