Transparent by design
The methodology is documented publicly so buyers and technical teams can inspect the factors instead of relying on black-box scoring.
13-Factor Methodology
Our 13-Factor AEO Methodology
AEO work is stronger when the scoring model is visible.
AI NYC uses a public 13-factor model that represents our best current understanding of what makes a site easier for answer engines to parse, trust, and cite. AEO is a new field and no methodology is proven, so we publish ours through @ainyc/aeo-audit so teams can inspect it, challenge it, and build on it. Much of the model draws from established SEO and content marketing principles, extended with technical signals specific to AI readability.
Core Principles
Built on SEO and content marketing foundations
Most of what works in AEO starts with strong traditional SEO and quality content marketing. Good site structure, useful content, and real authority are not new ideas. AEO layers on technical signals like structured data, AI-readable files, and entity clarity that help AI systems consume what is already there. Google's SEO Starter Guide covers many of these fundamentals.
Honest about uncertainty
AEO is an emerging field. Nobody knows exactly how AI models select which businesses to cite, and the landscape changes as models are retrained. Our 13-factor model is a working hypothesis based on research and observation, not a guaranteed formula. We update it as we learn.
Engineering-first remediation
We focus on the technical and content signals answer engines can actually consume, including schema, AI-readable assets, and clean direct-answer structure.
Outside content matters
Answer engines do not rely on your site alone. Third-party mentions, partner pages, directory profiles, editorial coverage, and other outside content help confirm that your on-site entity and service claims are real.
Built for competitive markets
The model is especially useful in markets like NYC, where multiple businesses compete for the same high-intent answer slots.
Factor Model
The public package and the agency use the same technical baseline.
The 13-factor model comes directly from the public documentation for @ainyc/aeo-audit. That means our client methodology starts with a published scoring model rather than an opaque internal checklist.
For local and location-sensitive work, we also evaluate an optional geographic layer: geographic signals.
The methodology also assumes that outside content matters. Strong structured data on your own site helps answer engines parse you, but third-party mentions, partner pages, directory profiles, and editorial references help them trust that the same entity exists beyond your own domain.
Structured Data (JSON-LD)
12%Presence of LocalBusiness, FAQPage, Service, and HowTo schemas.
Content Depth
10%Word count, heading hierarchy, paragraph structure, and list usage.
AI-Readable Content
10%llms.txt, llms-full.txt, robots.txt, and sitemap.xml availability.
E-E-A-T Signals
8%Author meta, trust pages, credentials, and review-oriented trust signals.
FAQ Content
8%FAQPage schema, question headings, and direct-answer formatting.
Citations & Authority
8%External references, authoritative links, and sameAs-style corroboration.
Schema Completeness
8%Property depth and richness across the structured data stack.
Entity Consistency
7%Naming consistency across schema, title tags, and on-page identity.
Content Freshness
7%dateModified, Last-Modified, sitemap dates, and current copyright signals.
Content Extractability
6%How easy the content is for answer engines to parse and cite.
Definition Blocks
6%Direct definitions, step lists, and HowTo-style explanation blocks.
Named Entities
6%Brand mentions, founder references, and proper-noun density.
AI Crawler Access
4%robots.txt rules for GPTBot, ClaudeBot, PerplexityBot, and peers.
How We Apply It
- Run the public audit or a deeper internal benchmark across your key URLs.
- Map the findings to buyer prompts, competitor surfaces, and citation gaps.
- Implement structural fixes: schema, entity clarity, trust pages, llms assets, and content architecture.
- Strengthen the off-site layer with outside content that corroborates the same entity, service, and market positioning.
- Re-test by prompt and platform to confirm movement in real AI answers.
See the methodology in action.
If you want the public narrative, read the open-source article. If you want the commercial application, go straight to the NYC agency page or run the free audit.