AI Visibility Model · Sample audit

Authority · Accuracy audit · Eyewear

AI engines name Warby Parker almost every time, but leave out the facts that define the company today.

An AI Visibility Model audit of what three leading AI engines say about Warby Parker: 10 locked questions, asked twice each in fresh sessions, 60 answers scored against seven brand facts.

Reference AIV-2026-02 · November 2026 · 3 AI engines · 60 answers · United States

91.4Visibility: the brand appears in nearly every answer
78.9Accuracy: answers match brand facts, on average
5%of answers state the NYSE listing (WRBY)
0%stale or made-up answers: the gap is omission

The read

This is a coverage problem, not an accuracy crisis, and it has a direct fix.

The engines do not invent anything. They leave facts out. One consistent, machine-readable source of company facts closes the gap, with re-measurement at Day 30, 60 and 90.
What’s working

Visibility is strong

100% presence on brand and buying questions. Home Try-On is the best-recognised fact, stated correctly in 65% of answers with no contradictions.

What’s broken

Corporate facts go missing

Listing, ticker, co-CEOs, founders and the full product range are left out of roughly 9 in 10 answers.

What we propose

Fix facts, then reinforce

Publish structured, brand-owned fact pages in 30 days, build third-party corroboration by Day 60, re-measure at Day 90.

Key findings

The consumer story lands. The company story doesn’t.

  • Omission, not error. Confident wrong answers are rare (1.7%) and no answer was stale. The engines hold the facts but don’t link them reliably to the brand.
  • Corporate facts are the gap. The NYSE listing is stated in 5% of answers, the co-CEOs and founders in 8.3%, the full product range in 11.7%.
  • Always present, not always at full strength. Warby Parker appears in every tracked question; competitors appear in 30% to 53%. Comparison answers lean on price and drop the differentiators.
  • Contact lenses go missing. Most answers leave them out, which makes the brand look frames-only to lens shoppers.
  • Engine differences are small. All three score highest on buying and brand questions and lowest on facts and category questions.

Brand facts · stated correctly

Fact by fact.

Brand factStatedLeft out
Free Home Try-On65%35%
Buy a Pair, Give a Pair43.3%56.7%
Full product range11.7%88.3%
Headquartered in NYC10%90%
Founders and 2010 founding8.3%90%
Co-CEO leadership8.3%90%
NYSE listing (WRBY)5%93.3%

Inside the full report

What the full audit covers.

AIV-2026-02 · AI Visibility ModelTable of contents
  1. ◆Executive briefVisibility and accuracy scores, the decision we need, and what is working, broken and proposed
  2. 01Detailed findings · fact by factSeven brand facts scored across 60 answers: stated, contradicted, left out
  3. 02Engine by engineVisibility and accuracy per engine, and accuracy by question type
  4. 03Competitive viewShare of answers naming each brand, and where the commercial exposure sits
  5. 04Technical checkCrawler access, llms.txt and structured data on the brand’s own site
  6. 05Action plan · 30 / 60 / 90Owned actions by window, and the re-measurement gates with targets
  7. 06Opportunity mapInvestor signal, Home Try-On, the giving program, leadership, contact lenses and buyer whitespace
  8. 07Risk register and methodologyRisks with mitigations, and the locked method that makes every re-measurement comparable
Full report

Request the full report.

Read the complete audit, including engine-by-engine scores, the technical check and the full 30/60/90 action plan.

For your company

See what AI engines say about your company.

We run the same locked audit on your brand: the questions your buyers, investors and partners ask, scored for visibility, authority and accuracy, with a plan to close the gaps and re-measure.

Samples are illustrative, use public information only, and are not endorsed by or affiliated with the companies shown.