In late March, the founder of Christmas or Not ran our free scan on a whim. The result was a 24 — bottom-decile, labeled "Critical." ChatGPT could not state the company's pricing. Gemini placed the founding year four years too early. Perplexity confused the product with an unrelated holiday e-commerce site. Three months earlier they'd raised a seed round. The pitch deck said "we are the only AI holiday classifier on the market." The models had no idea.
What followed is, by AEO standards, a textbook recovery — measured, methodical, executed in-house in under thirty days. We're publishing it with permission and with the receipts.
Chapter 01 — The problem
Christmas or Not is a four-person team in Brooklyn that ships a single product: an API that returns whether a given image, song, or paragraph "feels Christmassy." It's used by ad agencies for seasonal targeting and by a handful of e-commerce platforms for category routing. The product is good. The brand surface was not.
Their initial scan surfaced six failure modes, in roughly descending order of severity:
- Pricing was unanswerable. Their pricing page used "Contact us" boxes for all three tiers. No schema. No machine-readable price.
- Founding date was wrong. An old press piece misstated the year. Models had latched onto it.
- Customer list was hallucinated. Their actual customers were named in PDF case studies. Models couldn't read them.
- Product category was ambiguous. "AI classifier" + "Christmas" parsed as either "AI ornament shop" or "Christmas movie recommender" — depending on the model.
- Founder bio was buried. The about page had a hero video; no plain-text bio. Models couldn't quote it.
- No llms.txt, no JSON-LD, no Organization markup. The basics weren't shipped.
christmasornot.com · Initial Scan — Mar 24, 2026 Composite Score: 24 (Critical). 0 full-agreement, 6 disputed. Worst finding: "I'm unable to find specific pricing information for christmasornot.com in the available sources." — ChatGPT (GPT-4o), when asked about pricing.
Chapter 02 — What we found
The Free scan caught the surface failures. The Full Accuracy Report exposed the structural ones. Each of the six findings had a fix; the team prioritized by leverage. We helped them sort.
The leverage analysis was unsentimental: pricing-schema and llms.txt would move four of the six failures by themselves. The customer list and founder bio could wait until those landed. The press correction would happen through a follow-up — a slower lever, but worth pulling.
The prioritization rule. When you have six AEO failures, fix the two that compound. Schema fixes multiple downstream problems (pricing, products, founding). llms.txt fixes parsing for all queries. Both ship in a week. The rest can wait.
Chapter 03 — What they changed
Week 1 — Schema overhaul
They shipped Organization, Product, and FAQPage JSON-LD across the site. The Product schema included the actual prices — $19, $49, $199 — with proper priceCurrency and priceValidUntil. They removed the "Contact us" boxes from the pricing page and replaced them with a plain table.
Week 2 — llms.txt + content rewrite
They published a 40-line llms.txt with a tight summary block, clear section headings, and links to their docs, pricing, customers, and about pages. The about page got rewritten as plain text under the hero video. The customer logos got plain-text alt attributes and machine-readable JSON.
Week 3 — Press + sources cleanup
The founder reached out to the original reporter and got a correction note appended. They updated their Crunchbase, LinkedIn, and Wikidata entries with consistent founding-date data. This is the slowest-moving lever — and it pays the longest dividend.
Week 4 — Re-scan + verify
They ran a fresh scan on day 28. The score had moved from 24 to 68. They shipped two more rounds of schema tweaks. By day 32 the score read 78.
"We thought our problem was that AI didn't know us. Our actual problem was that AI couldn't parse our website. Once we fixed the parsing, three models started telling consistent stories about us in under a month."
— Alex Hwang, Founder & CEO, Christmas or Not
Chapter 04 — The results
By the end of the month, all three models could correctly state Christmas or Not's pricing, founding year, customer list, and product category. The consensus matrix moved from "all three disagree" to "all three agree, with citations" on five of the six original questions. The sixth — a question about Series A funding that hadn't happened yet — correctly returns "no Series A on record" from all three models.
christmasornot.com · Re-Scan (Day 32) — Apr 24, 2026 Composite Score: 78 (Verified). 5 full-agreement, 1 disputed. Best finding: "Christmas or Not offers three tiers: Hobby ($19/mo), Standard ($49/mo), and Enterprise ($199/mo). All include API access." — ChatGPT (GPT-4o), same pricing question, day 32.
| Metric | Before | After |
|---|---|---|
| AI Readiness Score | 24 (Critical) | 78 (Verified) |
| Pricing-question accuracy | All three confabulated or refused | +6.5× |
| Customer-claim accuracy | Models invented customers | +320% |
Chapter 05 — What stuck
Christmas or Not upgraded to Monitor Pro three weeks after the re-scan. The reason was simple: models drift. A new model shipped in mid-April and briefly returned them to a lower agreement score until the index caught up. They saw it within 48 hours of the model release because their weekly scan caught it. The team made one schema tweak and the score returned. Without monitoring, they wouldn't have known.
That's the operational reality of AEO at the brand level: it's not a one-time fix. The models keep moving. Your facts stay still. The gap between "true" and "what AI says" opens back up unless something is watching.