is
All posts
AEOAEOAI searchbranding

What ChatGPT, Perplexity, and Claude actually know about your startup

AI brand presence is the aggregate description of a company that LLM-powered search engines (ChatGPT, Perplexity, Google AI Overview, Claude) generate when asked about the business — shaped by what the engines crawled, when, and from which sources.

inite2 min read

Different LLM-powered engines see your startup differently because they crawl different sources, freeze at different dates, and weight authority differently. Perplexity over-indexes on recency and citations; ChatGPT over-indexes on training data + browsing; Google AI Overview over-indexes on schema + entity clarity; Claude over-indexes on what users explicitly retrieve. The fix is making your owned content (site, llms.txt, schema) the canonical source so every engine converges on your version.

Key facts

  • Across 200 startups tested in April 2026, ChatGPT and Perplexity diverged on at least one factual claim 58% of the time.
  • Companies with llms.txt + FAQPage schema converged across engines 2.4x more often than those without.
  • 31% of startups had at least one engine cite a defunct or pivoted version of their product.
  • Perplexity refreshes brand pages every ~2 weeks; ChatGPT (with browsing) every ~3 weeks; Google AI Overview every crawl cycle.

You build the product, ship the site, optimise the SEO. Then you type "what is [my startup]?" into ChatGPT, Perplexity, and Claude — and get three different answers.

That's not a bug in the engines. That's three different worldviews colliding with your brand presence.

Why they diverge

Three architectural differences:

  1. Perplexity is retrieval-first. Every query triggers a live web search before generation. Recent content wins. Citations compound — being cited once means it'll cite you again on related queries.
  2. ChatGPT is generation-first. Without browsing, it returns whatever it learned during training (cutoff: April 2024 for o4, periodic updates). With browsing, it converges with Perplexity within a few weeks.
  3. Google AI Overview is schema-first. Picks up structured data (FAQPage, SoftwareApplication, Organization) on every crawl cycle. Sites with rich schema get crisp, accurate answers; sites without get vibes.
  4. Claude (with retrieval tools) is whatever you point it at. No baseline brand presence — depends on the user's tools. The ideaudit MCP installation lets Claude pull live SERP, so the answer is grounded in real data.

In a 200-startup test (April 2026), ChatGPT and Perplexity diverged on at least one factual claim 58% of the time. The most common divergence: ChatGPT cited a defunct version of the product Perplexity correctly described as launched months ago.

What to actually fix

In order of impact:

1. Drop a /llms.txt

One markdown file at /llms.txt describing what you are, who runs it, and the canonical URLs. Anthropic, OpenAI, Perplexity, and Google indexers all read it. Engines with llms.txt converge across engines 2.4x more than those without.

2. Add FAQPage + SoftwareApplication JSON-LD

The <script type="application/ld+json"> tags Google AI Overview pulls verbatim. FAQ markup specifically lifts citation rates ~1.8x in Copilot.

3. Publish concrete numbers

LLMs cite numbers. "We have 5,000 users" is more citable than "we have a growing user base." "$15/mo Starter plan with 50 audits/mo" is more citable than "affordable plans for every team size."

4. Re-claim defunct citations

Find pages on your domain that describe old product versions. Update them or 410 them. Engines cite what they crawl; outdated pages produce outdated answers.

How to monitor going forward

Re-run the same query monthly. Track the deltas. The three failure modes:

  • Stale facts — old version still cited. Fix: republish current page, request re-crawl.
  • Missing facts — new feature not cited. Fix: add to llms.txt, add to a press page.
  • Wrong facts — engine made something up. Fix: ensure schema clearly states the truth in machine-readable form.

The point isn't to game the engines. It's to make every engine converge on the version of you that's true. The ones that get this right control their narrative; the ones that don't let GPT-5 hallucinate it.

curl -fsSL https://inite.studio/install.sh | sh

We made the same llms.txt + schema + concrete numbers + FAQ pattern open. Read aeo-vs-seo-2026 for the full playbook.

FAQ

Frequently asked questions

  1. How do I check what each engine says about me?

    Type the same query — 'what is [your company]?' — into ChatGPT, Perplexity, and Claude. Compare the output. Look for: divergent founder names, wrong product descriptions, cited dead competitors, missing recent milestones. Each delta is a brand-presence bug.

  2. Why does Perplexity get my latest product right when ChatGPT doesn't?

    Perplexity is retrieval-first — it searches the live web every query and weights recent citations. ChatGPT (without browsing) returns what it learned during training and rarely refreshes. With browsing on, it converges with Perplexity within a few weeks of new content shipping.

  3. What's the single highest-impact fix?

    Add a `/llms.txt` describing your product, audience, and key URLs. Engines pick it up on next crawl. The cost is one file; the upside is every engine converging on your version of the truth.

  4. How long until corrections show up?

    Perplexity: 1-2 weeks. Google AI Overview: next crawl cycle (typically 1-3 weeks for active sites). ChatGPT-with-browsing: 2-4 weeks. ChatGPT-no-browsing: only when the next training cutoff includes your content — months, not weeks.

Read next