Fundamentals
What Is Answer Engine Optimization?
Answer Engine Optimization (AEO) is the practice of structuring your website, content, and digital presence so AI-powered search engines can accurately understand, verify, and cite your business. When someone asks ChatGPT, Gemini, Perplexity, or Claude a question about your industry, AEO determines whether your business appears in the answer.
That is the short version. Here is the rest, including the scoring framework we use to measure it.
Why this exists now
For twenty years, the game was Google rankings. You optimized for keywords, earned backlinks, climbed the results page, and got clicks. That model still works, but a parallel system is growing fast.
According to Gartner's 2025 predictions, traditional search engine volume is expected to drop 25% by 2026. When someone asks ChatGPT "best accountant in Brooklyn" or Gemini "who does commercial roof coatings near me," the model returns a direct recommendation. Not a list of links. If your business is not in that recommendation, no amount of Google ranking helps with that specific user.
We track this shift using canonry, an open-source monitoring tool that asks AI models the same queries your customers would ask and records whether they mention a specific business. In one dataset (66 checks across 11 keywords for a local service business), branded queries got cited 82-90% of the time, while informational queries where the business had no content got cited 0% of the time. The gap is not gradual. It is binary.
How AEO differs from SEO
| SEO | AEO | |
|---|---|---|
| Goal | Rank in search results | Get cited in AI answers |
| Output | Blue links, snippets | Direct recommendation by name |
| Key signals | Backlinks, keywords, page speed | Structured data, entity clarity, extractability |
| Measurement | Rankings, clicks, impressions | Citation presence, competitor mentions, answer text |
| Update cycle | Continuous crawling | Model re-indexing (less predictable) |
The critical difference: in SEO, you compete for position on a results page. In AEO, you compete for inclusion in a single generated answer. There is no "page two." You are either mentioned or you are not.
The 13 factors: how we actually measure AEO
The @ainyc/aeo-audit tool scores any website across 13 weighted factors. You give it a URL, and it returns a score out of 100 with per-factor breakdowns. It is open source and runs from the command line. The scores correlate with actual citation outcomes in canonry monitoring data.
Here are all 13 factors, ordered by weight:
| Factor | Weight | What it measures |
|---|---|---|
| Structured Data (JSON-LD) | 12 | Schema markup types, property depth, entity connections |
| Content Depth | 10 | Word count, heading hierarchy, paragraph structure, lists |
| AI-Readable Content | 10 | llms.txt, robots.txt, sitemap, HTML link to llms.txt |
| E-E-A-T Signals | 8 | Author attribution, credentials, team pages, expertise claims |
| FAQ Content | 8 | Question-answer pairs, FAQPage schema, question-based headings |
| Citations | 8 | External references, source links, credibility markers |
| Schema Completeness | 8 | Coverage of required/recommended properties per schema type |
| Entity Consistency | 7 | NAP consistency, sameAs links, cross-platform entity verification |
| Content Freshness | 7 | Publish dates, update dates, recency signals |
| Content Extractability | 6 | Content-to-markup ratio, semantic HTML, page builder overhead |
| Definition Blocks | 6 | Opening definitions, "X is Y" statements, extractable descriptions |
| Named Entities | 6 | Business names, people, locations, specific services mentioned |
| AI Crawler Access | 4 | Robots.txt rules for GPTBot, ClaudeBot, OAI-SearchBot, Google-Extended |
Real scores: optimized vs unoptimized
Here is what the difference looks like in practice:
| Factor | Site A (90/100, gets cited) | Site B (48/100, never cited) |
|---|---|---|
| Structured Data | 100 (A+) | 42 (F) |
| AI-Readable Content | 100 (A+) | 56 (F) |
| Schema Completeness | 100 (A+) | 55 (F) |
| Entity Consistency | 86 (B) | 42 (F) |
| Content Extractability | 65 (D) | 45 (F) |
| Definition Blocks | 70 (C-) | 0 (F) |
| E-E-A-T Signals | 80 (B-) | 25 (F) |
Site A gets cited on 5 of 11 tracked keywords across 66 canonry monitoring runs. Site B gets cited on 0 of 23 keywords. Both are real sites tracked with canonry over two weeks.
The takeaway: even Site A has room to improve (65 on extractability, 70 on definition blocks). Perfect scores are not required for citation, but the floor is higher than most businesses expect.
The three layers of AEO
1. Technical signals
The structured data and crawlability layer:
- JSON-LD schema for LocalBusiness, Service, FAQPage, Person. See our schema guide for implementation details.
- llms.txt providing a machine-readable site summary for AI crawlers. Site A scores 100/100 partly because it has both llms.txt and llms-full.txt.
- Robots.txt allowing GPTBot, Google-Extended, and ClaudeBot.
- Clean HTML with semantic headings and minimal JavaScript rendering dependencies.
2. Content signals
Formatting for extraction:
- Definition blocks. Clear "X is Y" statements near the top of key pages. Site B scores 0/100 here because no page opens with a definition. This is the easiest factor to fix.
- Question headings. H2s phrased as questions matching how users query AI.
- Direct answers. First sentence under each heading answers the question. Then elaborate.
- Factual density. Concrete numbers over vague claims. Models prefer citable facts.
3. Authority signals
Confidence builders for AI models:
- Entity consistency across website, Google Business Profile, directories, and social media. BrightLocal's research covers why inconsistency erodes trust.
- Reviews and ratings on Google, Yelp, and industry platforms.
- E-E-A-T signals. Author attribution, credentials, expertise/authoritativeness/trust signals.
How to measure your AEO
Run your site through the audit:
npx @ainyc/aeo-audit@latest "https://yourbusiness.com" --format json
You get a score on all 13 factors, specific findings, and prioritized recommendations. The tool is open source on GitHub and published on npm.
For ongoing monitoring, canonry tracks whether AI models actually cite you for your target queries. The audit tells you what to fix. The monitoring tells you if it worked.
Or just run a free audit on our site if you want the quick version.
Where to start
- Audit your site. Get your baseline score across all 13 factors.
- Fix structured data first. Highest weight, clearest path to improvement.
- Add definition blocks. Lowest effort, highest ROI for sites scoring 0.
- Publish llms.txt. Five minutes of work for a meaningful AI readability signal.
- Start monitoring. Citation changes take weeks to appear. Start tracking now so you have data when they do.
The full methodology covers each step. The tools are free. The data is what matters.
FAQ
What is answer engine optimization?
Answer Engine Optimization (AEO) is the practice of structuring your website content, metadata, and digital presence so AI-powered search engines like ChatGPT, Gemini, Claude, and Perplexity can accurately understand, verify, and cite your business in their answers.
How is AEO different from SEO?
SEO optimizes for ranking in search results pages. AEO optimizes for citation in AI-generated answers. SEO focuses on links and clicks. AEO focuses on structured data, entity clarity, and content extractability. Our audit scoring framework measures 13 distinct factors, many of which are not part of traditional SEO.
Do I still need SEO if I do AEO?
Yes. AEO builds on SEO fundamentals. In our monitoring data, sites with strong SEO foundations respond faster to AEO improvements. But SEO alone is not enough because AI models weight structured data and content formatting differently than Google's ranking algorithm.
Is AEO the same as GEO?
GEO (Generative Engine Optimization) is another term for the same practice. Some use AEO, some use GEO, some use AI SEO. They all refer to optimizing for AI-powered search systems.
Try it yourself.
Run a free AEO audit to see how your site scores, or explore the tools and pages referenced in this article.