§ The framework
Identity · Language · Distribution · Data · Integrity

Five layers of New-Age SEO.

Search optimization used to mean ranking on a page. In an era of machine-readable answers, it means being understood, reinforced, trained on, remembered, and believed. The 5 Layers are the dimensions on which AI decides which brands to surface — and the map we use to diagnose and rebuild yours.

§ 01 — At a glance

A stack, not a checklist. Each layer depends on the one below.

05
Integrity be credible
Can AI confidently cite you?
04
Data be remembered
Are your facts structured & retrievable?
03
Distribution be trained on
Are you where AI learns from?
02
Language be reinforced
Do others describe you the same way you do?
01
Identity be understood
Who are you, and who are you for?
Layer
Name
Core theme
What AI needs to know
01
Identity
Be understood
Who are you, and who are you for?
02
Language
Be reinforced
Do others describe you the same way you describe yourself?
03
Distribution
Be trained on
Are you showing up where AI learns from?
04
Data
Be remembered
Are your facts structured and retrievable?
05
Integrity
Be credible
Can AI confidently cite and repeat you?
§ 02 — Each layer, in detail
01

Identity

Be understood.
Who are you, and who are you for?

The problem

Large language models only surface brands they can disambiguate. If an AI system cannot tell you apart from three similarly-named competitors — or cannot tell whether you sell to SMB or enterprise, US or Europe — it will default to the brand it can resolve. Ambiguity is the single largest cause of AI invisibility.

Identity is the foundation layer because every layer above it inherits the confusion. No volume of content, citations, or schema can compensate for a brand the model can't name with certainty.

What we work on

  • Canonical brand definition — a single sentence the model learns
  • Audience & ICP disambiguation (segment, geo, persona, use-case)
  • Category placement — the "we are an X that does Y" statement
  • Wikidata entity creation and claims architecture
  • Disambiguation collision resolution against look-alike brands
  • Consistent identity propagation across owned surfaces
Wikidata Entity graph Schema.org/Organization ICP taxonomy
02

Language

Be reinforced.
Do others describe you the same way you describe yourself?

The problem

Your brand doesn't own how it's described in AI answers — the collective internet does. When analysts, journalists, reviewers, forums, and competitors use a different vocabulary than your marketing site, the model averages across them and lands somewhere closer to the consensus than to your messaging. Language fragmentation is how brands lose control of their own narrative.

Reinforcement is the act of getting third-party vocabulary to match your canonical vocabulary — without writing a word of the third-party copy yourself.

What we work on

  • Messaging-vocabulary audit across owned vs. earned sources
  • Phrase-level drift detection across review sites and forums
  • Earned-media placement with vocabulary-aligned angles
  • Analyst and podcast briefing kits with canonical phrasing
  • Reddit, Quora, Stack Overflow, and G2 narrative seeding
  • Customer-quote and case-study language normalization
Earned media Analyst briefings Review platforms Community
03

Distribution

Be trained on.
Are you showing up where AI learns from?

The problem

Every frontier model is trained on a measurable subset of the internet — Common Crawl, Wikipedia, a short list of high-authority publishers, code repositories, Reddit, Stack Overflow, academic indexes. If your brand is absent from this corpus, it is structurally absent from the model's memory — regardless of how much content you publish on your own domain.

Distribution is the ground-truth layer. It determines whether you exist in the weights at all.

What we work on

  • Training-corpus presence audit (per-platform)
  • Wikipedia notability + citation architecture
  • High-authority publisher placement (tier-1 trade, tier-1 national)
  • Open-data contributions (GitHub, HuggingFace, arXiv)
  • Podcast transcript distribution to indexable surfaces
  • Syndication strategy for long-tail authority accumulation
Wikipedia Common Crawl Reddit GitHub Tier-1 press
04

Data

Be remembered.
Are your facts structured and retrievable?

The problem

Retrieval-augmented systems don't read your prose, they read your structure. Pricing, product specs, founding date, headquarters, headcount, integrations, supported platforms, certifications — every factual claim needs to live in a format a retrieval system can extract in milliseconds. Prose-only sites are effectively invisible to RAG pipelines.

Data is the layer where most B2B sites hemorrhage citations without ever knowing it.

What we work on

  • Structured-data deployment (JSON-LD, Schema.org, OpenGraph)
  • Fact-sheet pages optimized for passage-level extraction
  • Comparison tables, feature matrices, pricing grids
  • FAQ schema and question-answer pair engineering
  • Product catalog feeds and API documentation for agent access
  • llms.txt and AI-crawler-friendly content infrastructure
JSON-LD Schema.org llms.txt RAG-ready pages
05

Integrity

Be credible.
Can AI confidently cite and repeat you?

The problem

Frontier models now discount sources they deem low-credibility — thin content, affiliate-driven pages, sites with contradictory claims across pages, and authors without verifiable expertise. An AI may know you exist and know your facts, but still refuse to recommend you because the confidence signal is too low.

Integrity is the layer that separates "mentioned" from "recommended." It's the difference between being in the training set and being the answer.

What we work on

  • E-E-A-T audit (experience, expertise, authoritativeness, trust)
  • Author-bio, credential, and bylined-expertise architecture
  • Claim-consistency auditing across owned pages
  • Citation and primary-source attribution on every factual claim
  • Review-ecosystem hygiene (G2, Capterra, Trustpilot, Glassdoor)
  • Original research and data publication for authoritative citation
E-E-A-T Author schema Original research Review hygiene

The 5 Layers are what we optimize. The Bite Method is how.

Every engagement begins with a diagnostic across all five layers. We publish a per-layer maturity score, identify which layers are the leverage points for your category, and sequence the roadmap accordingly.

See the Bite Method → Request a layer audit →