E-E-A-T Trust Architecture
Author graphs, Organization/Person schema, and verifiable expertise signals.
Explore E-E-A-T →HEG v2.4 · Deterministic search architecture · English L0
Valerion Tech: Hierarchical Entity Graph
A first-principles entity network that drives information entropy toward zero (H(X) → 0) and stops link bleed in modern search and generative engines.
Brand disambiguation. Valerion Tech (
valerion.tech) is a technical research and web-architecture resource. It has no affiliation with the consumer electronics / laser-projector brand Valerion (valerion.com / AWOL Vision).
DAG nodesPublished HEG map (sitemap)
Link entropy H(X)→ 0 (design goal)
L3 CrUX field dataCrUX field datasets
Core Web VitalsCWV targets (field + lab)
Ontological model
What is Hierarchical Entity Graph (HEG)?
HEG is a strict four-layer architecture that makes every URL ontologically accountable. Search engines and generative engines (GEO) receive unambiguous entity relations without topical noise.
L1 Core entities
Four pillars
E-E-A-T, Technical SEO, Topical Authority, User Signals — bidirectional anchors.
L2 Supporting
Method essays
Parent-up linking, max two siblings — no link bleed.
L3 Evidence
Empirical datasets
CrUX benchmarks and reproducible studies for information gain.
L1 pillars
The four English L1 hubs
Technical SEO Infrastructure
Prerendered HTML, Core Web Vitals, crawl budget, and edge delivery.
Explore Technical SEO →Topical Authority & Entities
Semantic siloing and JSON-LD @graph structures without topical bleeding.
Explore Topical Authority →User Signals & Intent
Answer-first GEO definitions, intent measurement, and pogo-stick reduction.
Explore User Signals →How HEG works in practice
From ontology to crawlable HTML
Most “entity SEO” decks stop at vocabulary. HEG is operational: every public URL declares its layer, its parent, and the evidence that justifies ranking. Generative engines and classical crawlers both consume the same HTML — there is no cloaked bot shell and no empty SPA for unknown user-agents.
Information entropy in the internal link graph is driven toward zero by forbidding cross-silo shortcuts. An L2 essay links up to its L1 pillar and sideways to at most two siblings. L3 evidence pages never become generic resource dumps; they stay anchored to the claim they support.
Phase 1
Machine-readable root
Prerendered HTML, stable canonicals, reciprocal hreflang, and a JSON-LD @graph that names Organization, Person, WebPage, and BreadcrumbList on first byte.
Phase 2
Pillar accountability
Four L1 hubs own disjoint problem spaces — trust, infrastructure, topical graphs, and user feedback — so authority does not dilute across marketing slogans.
Phase 3
Method without bleed
L2 essays teach one technique deeply. Parent-up linking keeps PageRank and topical relevance inside the silo that earned them.
Phase 4
Evidence you can replay
CrUX field datasets, crawler policy checks, and schema validators turn claims into reproducible artefacts instead of screenshots.
Why English L0 exists
A full start page — not a redirect
The English start page at /en/ is the reciprocal of the Swedish home. It frames Valerion Tech as an independent research publisher, disambiguates the projector brand, and routes readers into the EN L0 hub and free tools without forcing a bounce through a guide-only URL.
Swedish remains the densest corpus today; English mirrors the same four pillars so hreflang pairs stay honest. When a page has no EN twin yet, language controls fall back to this start page rather than inventing a soft-404 SPA route.
- Open the EN L0 Entity Hub for the architecture narrative and child pillars.
- Use the AI Crawler Access Checker to see GPTBot, Claude*, Perplexity*, and Bing AI preferences on any public URL.
- Run the Schema Entity Validator to score Organization/Person/@id consistency before you ship.
- Prefer same-silo links only — L1 hubs and their own L2/L3 children — when you cite neighbouring essays.
Operating principles
Constraints that keep the graph honest
HEG rejects infinite related-posts widgets. Every published EN or SV URL should answer: which layer am I, who is my parent, what evidence do I add, and which user-agent receives the same HTML? Those questions map directly to crawl-budget hygiene, Core Web Vitals budgets, and E-E-A-T graph integrity.
Edge tooling on Cloudflare Workers enforces SSRF-safe fetches for the public checkers, injects a single people RUM beacon for humans, and serves prerendered snapshots to every user-agent — including Chrome, curl, and python-requests — so reviewers never depend on client-side forms alone.
If you are new to the model, start at the L0 hub, pick one pillar that matches your bottleneck (trust, infrastructure, topical coverage, or engagement), and only then descend into L2 method essays. That order preserves topical focus and keeps internal PageRank where the entity graph needs it.
Tools
Interactive EN tools
AI Crawler Access Checker
Inspect how major AI crawlers are allowed or blocked on a domain — SSRF-safe edge API.
Run checker →Schema Entity Validator
Validate E-E-A-T-oriented JSON-LD entity graphs against schema consensus rules.
Validate schema →Publisher
Rasmus Bastrup
Principal Systems Architect and founder of Valerion Tech. HEG treats SEO as applied systems architecture: closed knowledge graphs, verifiable authorship, and reproducible benchmarks — not guesswork.