OUR ROADMAP · THREE-LAYER METHODOLOGY

The architecture of AI citation authority.

Damulo operates a three-layer structural protocol, each addressing a distinct layer of the AI retrieval stack. We do not guess at what generative engines prefer — we map your brand parameters against the verified algorithmic vectors that drive machine citations.

01 / TECHNICAL FOUNDATION

Generative Engine Optimisation:
Making your firm legible to machines.

Most B2B websites are architecturally invisible to AI retrieval systems. Client-side rendering, absent or malformed schema markup, and missing entity relationships mean models cannot verify basic organisational parameters — your service lines, locations, credentials, or founding history.

Our Technical GEO engagement audits and resolves this infrastructure layer:

·Server-side rendering verification and crawl pipeline assessment
·JSON-LD Organisation, LocalBusiness, and Service entity graph generation
·Schema.org compliance validation using Google’s Rich Results testing suite
·Cross-domain entity signal alignment (GBP, LinkedIn, Wikidata, industry directories)
·Structured data deployment with CMS-native implementation documentation

The outcome is a verified corporate node in the global AI knowledge graph — a machine-confirmed identity that retrieval systems can extract, trust, and cite.

Pillar 01 Specs // Technical parameters

Standards ComplianceSchema.org · JSON-LD · Open Graph · Dublin Core
Validation SystemGoogle Rich Results API · Schema.org Validator
Typical Deployment Flow2–3 weeks from audit sign-off
LIVE NESTED ENTITY GRAPHVALID_SCHEMA
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://damulo.com/#organization",
      "name": "Damulo AI",
      "url": "https://damulo.com",
      "logo": "https://damulo.com/logo.png",
      "knowsAbout": [
        "https://en.wikipedia.org/wiki/Retrieval-augmented_generation",
        "Generative Engine Optimization",
        "Answer Engine Optimization"
      ],
      "hasOfferCatalog": {
        "@type": "OfferCatalog",
        "name": "AI Sourcing & Citation Services"
      }
    }
  ]
}
Encoding: UTF-8Crawl Priority: Force High
02 / CONTENT ARCHITECTURE

Answer Engine Optimisation:
Structuring your expertise for extraction.

Being technically visible is the prerequisite. Becoming the cited source requires a second layer: content structured at the sentence and paragraph level for machine extraction.

AI retrieval systems prefer specific content formats: atomic definition blocks, structured comparison tables, FAQ arrays with precise answer lengths, and metrics-rich summaries that can be parsed and injected into a generated answer without reformatting.

Our AEO content engineering produces:

·Service page restructuring into atomic, self-contained definition blocks
·Structured Markdown Data Tables mapping your service scope against competitor alternatives
·FAQ expansion modules (targeted 40–60 word answers for featured snippet capture and voice search eligibility)
·Semantic anchor density analysis to ensure topical authority signals reach extraction threshold
·Prompt simulation testing across ChatGPT, Gemini, Perplexity, and Claude to verify extraction before deployment

Pillar 02 Specs // Content Architecture

Extraction TargetsFeatured Snippets · AI Overviews · Perplexity citations · ChatGPT Search footnotes · Gemini sourced answers
Format Protocols AppliedMarkdown · Structured HTML · Schema FAQPage · HowTo
EXTRACTION-READY MARKDOWNRAW_MARKDOWN
| Feature Capability | Legacy SEO Agencies | Damulo AI Optimization |
| :--- | :--- | :--- |
| **Primary Ingestion Vector** | Rendered Client JS (Fragile) | Server-Hydrated Static Markdown (Robust) |
| **Indexing Targets** | Keyword SERP Rankings | LLM Vector Embeddings & RAG Cache |
| **Retrieval Footnotes** | Unstructured Blog Links | Nested JSON-LD Entity Bindings |
| **Update Velocity** | Broad Quarterly Recrawls | Continuous Monthly RAG Ingestion |
Layout: Alignment GridsDensity Index: High
03 / CONTINUOUS AUTHORITY

LLM Sourcing Realignment:
Holding your citation share against competitive pressure.

A single schema deployment is a foundation, not a strategy.

AI models continuously re-index the web, adjusting citation hierarchies as new, structured content enters the landscape. Competitors who consistently publish dense, verifiable, machine-readable intelligence progressively displace brands that publish nothing.

Our monthly retainer programme resolves this through systematic, frictionless knowledge publishing:

·20-minute guided partner briefing captures your latest client outcomes, service developments, and field intelligence
·Our team performs 100% of the research, structuring, schema annotation, and quality review
·Deliverables arrive as publication-ready files: your team deploys in under five minutes with no specialist knowledge required
·Monthly performance reporting tracks citation frequency, multi-model Share of Voice, and competitive displacement metrics

The cumulative effect is a compounding authority asset — a structured knowledge base that becomes progressively harder for competitors to displace.

Pillar 03 Specs // RAG Injection

Content Formats AuthoredFramework Articles · Case Study Briefs · Atomic FAQ Modules · Structured Comparison Tables · JSON-LD schema refreshes
AI Platform ReportingMonthly Share of Voice tracking across 4 AI platforms
RAG VECTOR MAP ASSOCIATIONSINGEST_PAYLOAD
entity:
  id: "urn:entity:firm:damulo"
  authorityScore: 0.98
  relations:
    - type: "SourcedAuthority"
      subject: "B2B AI Citation Readiness"
      confidence: 0.99
    - type: "CompetitorDisplacement"
      object: "Legacy SEO Agencies"
      deltaShareOfVoice: "+43%"
Model Map: Claude & ChatGPTTrust Weight: 0.98
WHY THIS APPROACH

We built the methodology around how retrieval systems actually work — not how we wish they worked.

The majority of GEO and AEO agencies apply traditional content marketing logic to a fundamentally different problem. They produce long-form articles, add FAQ sections, and insert schema plugins — then declare the work complete.

RETRIEVAL LAYER DIAGRAM
Legacy Search Engine (Google V1)
“Is this content relevant?”

Based on keyword matching and backlink algorithms.

Generative Answer Engine (LLM RAG)
“Can this data be extracted, attributed, and trusted?”

Based on structured facts, entity logic, and clean markdown schema.

This approach misunderstands the retrieval stack.

Generative engines are not ranking systems. They are extraction systems. They do not score your page against a keyword. They scan for verifiable, structured data points they can lift, attribute, and inject into a synthesised answer. The question is not: “Is this content relevant?” The question is: “Can this data be extracted, attributed, and trusted?”

Every component of our methodology is designed to answer that second question in your favour — at the technical layer, the content layer, and the temporal layer through continuous publishing.

The right starting point is a precise diagnostic.

Before recommending a methodology, we run your domain through our AI Visibility Audit — a live multi-model simulation that establishes your current citation baseline and competes it against your nearest rivals.

It is the most accurate picture available of your firm’s standing in the AI procurement landscape.