Technical On-Site AEO
The technical on-site layer of AEO, made transparent.
The 16-factor model is our scoring framework for the technical on-site layer of Answer Engine Optimization, the part of the work that lives on your own website. It is one piece of a larger picture. Effective AEO is holistic and depends on four layers working together: traditional SEO fundamentals, technical site optimizations (what the 16-factor model measures), content depth and quality, and external linking and off-site corroboration. We publish the full model so teams can inspect it, challenge it, and build on it. It is grounded in proven SEO and structured-data fundamentals, and we sharpen it as answer engines evolve.
Core Principles
Strong answer engine performance requires four layers working together: traditional SEO fundamentals, technical site optimizations, substantive content, and external linking. The 16-factor model covers only the technical on-site layer. The other three layers sit alongside it in any full engagement, and a strong score on the 16 factors alone is not enough on its own.
The on-site technical methodology is documented publicly so buyers and technical teams can inspect the factors instead of relying on black-box scoring.
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. The 16-factor model 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.
The signals our 16-factor on-site model scores are well established, not guesswork. They come from proven SEO and structured-data fundamentals, line up with Google's own AI optimization guidance, and match what we see cited across ChatGPT, Claude, Gemini, Perplexity, and Copilot. As answer engines evolve, we refine how each factor is weighted and fold in new signals, so the model sharpens over time.
Within the on-site technical layer, we focus on signals answer engines can actually consume, including schema, AI-readable assets, and clean direct-answer structure.
Answer engines do not rely on your site alone. Third-party mentions, partner pages, directory profiles, editorial coverage, and inbound links help confirm that your on-site entity and service claims are real. The 16-factor model does not score these external signals, but our full engagements address them as a separate workstream.
The model is especially useful in markets like NYC, where multiple businesses compete for the same high-intent answer slots.
On-Site Factor Model
The 16-factor model scores only the technical signals that live on your own website. For local work, we also evaluate geographic signals. Traditional SEO fundamentals, content depth, and external linking sit alongside this model as separate workstreams in a full engagement. Per-platform retrieval and citation behavior across ChatGPT, Claude, Perplexity, Gemini, and Copilot is covered separately, since those mechanics sit outside the on-site layer.
JSON-LD presence, priority schema types, and property depth.
Word count, heading hierarchy, paragraph structure, and list usage.
Availability and depth of AI-readable files, robots.txt, sitemap, and Markdown alternates.
Author and credential signals, reviews, trust links, and organization people.
Visible question-and-answer content and matching FAQPage schema.
Citations and links to relevant, authoritative external sources.
Expected properties and relationships across detected schema types.
Valid JSON-LD syntax, schema types, and duplicate singleton checks.
Consistent entity details across page content, metadata, and schema.
Dates and freshness signals in content, schema, headers, and sitemaps.
Semantic HTML and content structure that can be read without interface chrome.
Clear definition-style passages that introduce a topic directly.
robots.txt access and declared AI-use preferences for provider controls.
Clear entity naming and supporting entity context in content and schema.
One H1, descriptive image alt text, meta description, and canonical URL.
Indexing and snippet directives that preserve Google AI feature eligibility.
Industry Guidance
In 2026, Google released its AI Features and Your Website guide, an official perspective on optimizing for AI-driven search experiences. We read it closely and use it as one of several inputs that shape the 16-factor model. Where it aligns with what we observe across other AI systems, we agree. Where it speaks only for Google's own properties, we keep optionality.
Read Google's AI Optimization Guide
The guide is authoritative for Google Search and Gemini. ChatGPT (OpenAI), Claude (Anthropic), Perplexity, and Microsoft Copilot each operate their own retrieval pipelines, with different crawling, indexing, and citation behaviors. Optimizing only to Google's specification shrinks the coverage we can build across the broader AI answer surface.
Google's guide states it does not recommend llms.txt. We have observed signals across other AI systems and crawler behaviors that suggest llms.txt and llms-full.txt are part of a useful redundancy layer for non-Google retrieval, and the publishing cost is near-zero. Until the broader retrieval picture converges, we keep them in the on-site stack and frame their value as observed, not confirmed.
Most of what the 16-factor model scores, including JSON-LD structured data, entity consistency, content depth, E-E-A-T, FAQ schema, external citations, content freshness, and AI crawler access, is endorsed by Google's guide and matches what we observe across other AI systems. The model is built around the signals that show up in multiple places, not a single vendor's playbook.
How We Apply It
The 16-factor model is the diagnostic layer. Canonry, our source-available AEO operating system, is where we run the work: it records the technical baseline, watches how AI answers move week over week, and gives every fix a feedback signal across ChatGPT, Claude, Gemini, Perplexity, and Copilot. Together they turn AEO from a static PDF into a tracked workstream with a live citation surface attached to it.