[ METHOD ] HOW WE MANUFACTURE VISIBILITY
Visibility inside generative engines is engineered, not wished for. Cadive runs the same four-phase system on every project — Audit, Architect, Engineer, Amplify — turning a brand into digital infrastructure that language models can ingest, trust, and cite as a primary source.
[01] PHASE ONE
Audit
We measure where you exist — and where you vanish — inside the engines your buyers now ask first.
Before a line of code changes, we benchmark your share of answers across five-plus generative engines against the brands currently winning your category. We map how language models name you, what they get wrong, and which entities they associate with your expertise. The audit turns invisibility into a measurable starting position.
- → AI-visibility benchmark across five-plus generative engines
- → Competitive share-of-answer analysis vs. category leaders
- → Entity recognition and disambiguation report
- → Prioritised gap map: where you are absent from the answer
[02] PHASE TWO
Architect
We design the entity model, semantic structure, and schema graph that make your expertise unmistakable.
We model your brand as a set of explicit entities and relationships, then design the information architecture, heading hierarchy, and JSON-LD graph that expresses them. This is where meaning is decided — what you are, what you know, and how every passage connects to the next. The architecture is engineered for chunked retrieval, not just human reading.
- → Knowledge-graph entity model and relationship map
- → Question-led information architecture and URL strategy
- → Schema.org graph designed for retrieval, not only rich results
- → Citable-passage specification for every key page
[03] PHASE THREE
Engineer
We build the site: static-first, accessible, sub-second, with GEO baked into every line rather than bolted on after.
We translate the architecture into a real, fast, semantic property. Server-rendered HTML carries the full meaning; 3D and motion are layered on as progressive enhancement and never required to read the page. Every build targets Lighthouse 95+ with the experience fully intact — because the foundations that make a site legible to machines are the same ones classical search rewards.
- → Server-rendered semantic HTML as the equivalent-content baseline
- → Structured data wired to the live entity graph
- → Progressive enhancement: 3D and motion layered, never required
- → Lighthouse 95+ performance, accessibility, and SEO budgets
[04] PHASE FOUR
Amplify
We earn the attribution signals that teach models to treat you as a primary source — then monitor your share of answers.
Visibility compounds when external signals confirm what your site claims. We inject machine-readable expertise (E-E-A-T), pursue citations from sources language models already trust, and mark provenance and freshness so retrieval confidence rises. Then we monitor — tracking your share of answers over time and feeding what we learn back into the next cycle.
- → Author, expertise, and credential signals in structured data
- → Off-site citation and digital-PR strategy for LLM-trusted sources
- → Provenance and freshness markers for retrieval confidence
- → Continuous citation monitoring and share-of-answer reporting
[ ETHICS ] DUAL RENDERING
Two audiences. One truth. No cloaking.
Every Cadive property renders for two readers at once. Humans receive the high-fidelity layer — 3D, motion, and the craft that wins on design. Machines receive clean, high-density structured semantic text optimised for token-efficient ingestion. The human experience is progressive enhancement layered over a complete, server-rendered semantic baseline — never a replacement for it.
Both layers carry the same truth. We never serve one claim to a crawler and a different claim to a person. This equivalent-content principle is the line between legitimate optimisation and manipulation — and it is the foundation of our Dual-Rendering Architecture.
[ PRIMITIVES ] THE UNITS OF CITABILITY
Four primitives every page is built from.
Citable passages
Self-contained, factual blocks written to be quoted verbatim. A model can lift one paragraph and answer correctly without the surrounding page.
Entity mapping
Explicit definitions of what your brand is and how it relates to people, concepts, and competitors — so the Knowledge Graph recognises you as one clear entity.
Structured data
JSON-LD engineered for meaning and retrieval, not just rich-result eligibility. The machine layer that states your facts in a language models parse without ambiguity.
Attribution signals
Machine-readable expertise and provenance markers that raise a model's confidence to cite you as the primary source rather than a secondary mention.
[ FAQ ] METHOD QUESTIONS
How the system holds up under scrutiny.
How long does the methodology take to show results?
Is dual rendering the same as cloaking?
Do I need to rebuild my entire site to follow this methodology?
START
Run the system on your brand.
Tell us your category. We'll start where the methodology starts — an audit of where you stand inside the engines today.