Great brands lose rankings not because they lack expertise — but because their expertise is invisible to Google and AI search. We engineer the trust signals that make your authority provable, citable, and ranking-ready.
Dr. Sara Morrison
Chief SEO Strategist · 14 years in search
12
Pubs
4
Talks
8
Citations
Trust score
↑ +34 pts in 30d
4 pillars
Experience · Expertise · Authoritativeness · Trust
2×
higher citation rate in AI search (Google SGE / ChatGPT / Perplexity)
14–21 days
from trust audit to roadmap and first surfaced signals
100%
measurable — every trust signal mapped to a ranking factor
EEAT at a Glance
EEAT isn't a score you tweak. It's a system that makes your real expertise provable — so search engines and AI can cite you with confidence.
First-hand, demonstrable involvement — not just theoretical knowledge about the topic
Deep domain skill, proven credentials, and the body of work that makes that expertise verifiable
Recognition and reference by trusted third parties — citations, mentions, and industry signals
Site security, transparency, accuracy, source citations, and the signals Google treats as non-negotiable
The visible artefacts — bios, testimonials, case studies, credentials — that make EEAT scannable, not asserted
How well your signals are structured so Google AND AI search (SGE, Perplexity, ChatGPT) can cite your brand
When You Need EEAT Improvement
Most EEAT problems aren't a lack of expertise — they're a failure to make that expertise visible, provable, and citable.
Founders / experts behind the content have no author profiles, bios, or verifiable credentials on the site
Articles read like they were written by no one in particular — no perspective, no experience, no authorship
Finance, health, legal, insurance — Google applies stricter EEAT standards and you're getting outranked by less-expert competitors
ChatGPT / Perplexity / Google SGE reference competitors for queries where you're genuinely the expert
Third-party mentions, reviews, and citations exist — but nothing is structured into signals Google can read
Helpful Content, Core, or Reviews updates hit you hard — because trust signals were thin even though the content wasn't
What EEAT Improvement Covers
Not a theory lecture. A concrete engineering project that turns invisible expertise into machine-readable trust signals.
Full inventory of every EEAT signal on your site — what's there, what's missing, and what's working against you
Turn the humans behind your content into verifiable, citable, schema-linked experts
Structured proof artefacts — the testimonials, case studies, awards, and certifications that back every claim
Every claim sourced, every fact dated, every statistic current — the content-quality layer that EEAT actually measures
Who you are, how you work, how you make money — the signals Google associates with trustworthy sites
Structure your content so AI search engines (SGE, Perplexity, ChatGPT) can cite your brand by default
Core Use Cases
Every EEAT project starts in a different place — pick the shape that matches your reality.
Best for
Consultant-, founder-, or practitioner-led brands
Problem solved
The real expert is behind the curtain — Google has no way to connect the content to a credentialed human
Expected outcome
Author schema, bio architecture, and external profile linking that make your expert the named source
Best for
Sites with content but missing foundational trust pages
Problem solved
No editorial policy, thin About page, weak contact info, unclear ownership — basic trust signals missing
Expected outcome
Complete trust architecture: About, contact, editorial, privacy, methodology — structured for Google's quality rater guidelines
Best for
Brands with wins that aren't visible: case studies, testimonials, credentials unused
Problem solved
The proof exists — it's scattered across decks, LinkedIn, email testimonials — never surfaced on the site
Expected outcome
Structured proof layer: case study library, testimonial architecture, credential surfacing with schema
Best for
Content-heavy sites post Helpful Content / Core updates
Problem solved
Content looks fine but lacks sources, citations, dates, and the quality signals Google now weighs heavily
Expected outcome
Per-article source audit, factual refresh, citation integration, last-updated metadata — rebuilt trust per page
Best for
Brands preparing for Google SGE, ChatGPT, Perplexity visibility
Problem solved
AI search cites competitors for queries where you're the real expert — because your signals aren't structured for retrieval
Expected outcome
Entity markup, citable statements, topical authority clusters, and Knowledge Graph readiness
EEAT by Business Goal
The shape of the problem changes the shape of the solution. Trace your goal here.
Goal
Need more trust
Solution
Improve proof signals
Goal
Need more authority
Solution
Enhance expert presence
Goal
Need stronger credibility
Solution
Optimise full EEAT stack
Goal
Need AI search visibility
Solution
Improve citation readiness
Goal
Need YMYL-level trust
Solution
Full EEAT rebuild
Why Brands Lack Search Trust
Most brands we audit have deeper expertise than their competitors. The problem isn't earning trust — it's engineering it so Google and AI can actually see it.
Avana Hub EEAT Framework
A repeatable engineering system. Not a content-writing exercise — a structural rebuild that ends with measurable, verifiable trust.
Map every existing EEAT signal: authors, proof, trust pages, citations, schema
Validate the claims: credentials checked, experience real, testimonials sourced
Make verified expertise visible with bios, schema, proof pages, and citable claims
Fill gaps — add citations, case studies, editorial policy, and Knowledge Graph markup
Ongoing freshness, new proof integration, author attribution discipline, schema monitoring
Process Timeline
A sequenced rollout — not a 6-month engagement. We surface the foundational trust signals fast, then layer depth on top.
Full inventory: authors, proof artefacts, trust pages, schema, citations, external reputation
What's missing, what's misrepresented, what's invisible — mapped to EEAT pillars and YMYL rules
Impact × effort scoring — which signals move credibility fastest, which need long-term work
Author schema, proof architecture, trust pages, citation integration, content metadata, entity markup
Track trust-score movement, citation rate in AI search, ranking shifts, and brand entity signals
Sample Output
Not a theory slide. A before/after of the tangible signals we add — and the measurable lift they produce.
Authors surfaced
6
Trust pages added
4
Claims sourced
142
Citation lift (AI)
+87%
What You Get
Not a PDF of theory. A working trust system you can hand to engineers, writers, or your in-house team tomorrow.
Full trust signal inventory scored against Google's Quality Rater Guidelines
Prioritised list of missing or underperforming trust pages, policies, and signals
Bios, schema, credential verification, and external profile linking for every expert
Structured proof layer — case studies, testimonials, awards, review schema
Source citations, fact-check passes, last-updated metadata, and accuracy improvements
Entity markup, citable statement architecture, and AI-search readiness roadmap
Pricing Plans
From a focused trust audit to a full authority rebuild with ongoing signal maintenance. Scoped to your current EEAT baseline and competitive niche.
Full trust signal audit with prioritised gap report and implementation roadmap
Full EEAT engineering: audit + implementation of trust architecture and author layer
Quarterly audits, continuous signal maintenance, and author-layer scaling
Tailored to your needs
Frequently Asked
Related Services
Get Started
Your real expertise exists. We engineer it into trust signals Google respects and AI search cites — so your brand becomes the reference in your category, not a footnote.
Overall Trust Score