Axiom Wave — AEO & GEO Readiness Dashboard

What Are AEO & GEO?

The way people discover information is undergoing a fundamental transformation. Traditional search — typing keywords and clicking through blue links — is giving way to AI-powered answer engines that synthesise direct responses from multiple sources. This shift demands two new optimisation disciplines alongside traditional SEO:

AEO

Answer Engine Optimisation — structuring content so AI systems can extract and present direct answers in featured snippets, voice search, and AI Overviews

GEO

Generative Engine Optimisation — crafting authoritative, citable content that AI platforms like ChatGPT, Perplexity, and Gemini cite when generating synthesised responses

SEO

Search Engine Optimisation — the essential foundation. If you don't rank well in traditional search, AI platforms won't find you either

The Triple-Threat Strategy: SEO builds your base, AEO gives you precision in direct answers, and GEO earns you AI presence. These are layers, not separate strategies. Axiom Wave needs all three working together to maintain visibility as AI reshapes discovery.

Axiom Wave Overall AEO/GEO Readiness

Based on analysis of 4,562 indexable pages across the Axiom Wave website, scored against current industry best practices from Ahrefs, Semrush, Moz, and leading GEO research.

Composite Readiness Score: 61/100 — Axiom Wave has a strong technical and schema foundation but significant gaps in answer-readiness content, FAQ coverage, and content depth that must be addressed to compete in AI-powered discovery.

Site Crawl Snapshot

The AI Search Revolution: Why This Matters Now

AI-powered search is no longer an emerging trend — it is the present reality. Gartner predicts a 25% drop in traditional search engine volume by 2026, and the data supports this shift accelerating faster than anticipated.

How People Actually Use ChatGPT

OpenAI's September 2025 research paper, studying billions of ChatGPT messages, reveals critical insights for Axiom Wave's strategy:

Source: OpenAI "How People Use ChatGPT" (Sep 2025). ~80% of usage falls into Practical Guidance, Seeking Information, and Writing.

Key implication for Axiom Wave: "Seeking Information" — the category most analogous to web search — is the second-largest use case. Users are increasingly asking AI assistants questions like "What is the best mobile plan for international travel?" or "How does 5G home broadband compare to fibre?" — exactly the queries a telecoms provider must be optimised for.

From Keywords to Conversations: The Telecoms Opportunity

Traditional keyword-based search is evolving into natural-language, conversational queries. Here's how this affects Axiom Wave:

The shift is clear: AI-powered search demands content that directly answers specific, scenario-based questions — not generic keyword-stuffed pages. When an AI Overview appears, the #1 traditional result loses 34.5% of its clicks. Only 52% of sources cited in AI Overviews come from the top 10 search results — meaning well-structured, authoritative content can leapfrog traditional rankings.

Six GEO Trends Shaping 2026

# Trend Implication for Axiom Wave
1 AI visibility as core KPI — brands will track AI citations alongside web traffic Axiom Wave needs AI visibility monitoring tooling immediately
2 Content depth over keyword volume — authority and evidence-backed content wins 29% of pages are thin content (<300 words) — a critical gap
3 Brand mentions replace rankings — no "positions" in AI, only citations Axiom Wave needs a proactive brand-mention strategy
4 Personalised AI answers — context-aware, user-specific responses Content must address specific personas and scenarios
5 Platform loyalty forming — users sticking to preferred AI engines Must optimise for ChatGPT, Perplexity, Gemini, AND Google AI Overviews
6 Local + personalised GEO — 84% of consumers search for local businesses daily Market-specific content for each European and African operation

Content Depth Analysis

AEO and GEO best practices demand content that is comprehensive enough for AI to extract meaningful answers. Pages with fewer than 300 words are considered "thin" and unlikely to be cited by AI systems.

Finding: Nearly 1 in 3 indexable pages (29.1%) has fewer than 300 words. The median word count is just 680. For AEO/GEO, deep, authoritative content (1,000+ words) is ideal for earning AI citations — yet only 34.8% of pages meet this threshold.

Page Structure & Heading Hierarchy

Clear H1→H2→H3 heading hierarchy is essential for AI systems to parse content structure. Missing headings reduce a page's chances of being selected for AI answers.

Assessment: H1 and H2 coverage at 77% is decent but leaves 1,047 indexable pages without an H1 — these pages are essentially invisible to AI heading-based parsing. The 30.4% missing meta descriptions also reduce snippet eligibility.

Internal Linking Health

Strong internal linking helps AI crawlers discover and contextualise content. Orphan pages (0–1 inlinks) are especially vulnerable to being missed by both search engines and AI systems.

Finding: 594 pages (15.6%) are orphaned with 0–1 inlinks. The median of just 4 inlinks shows that beyond sitewide navigation, most pages receive minimal contextual cross-linking. Additionally, 201 pages have zero outlinks — dead ends that hinder AI from understanding topical relationships.

Readability Distribution

AI systems favour clear, accessible content. Readability levels should target "Normal" to "Fairly Easy" for consumer-facing telecoms content.

Positive: 85.6% of content falls in the "Very Easy" to "Normal" range — well-suited for AI extraction. The 14.4% of harder content likely sits in investor/regulatory sections and may benefit from plain-language summaries.

Crawl Depth Distribution

Content buried beyond 3 clicks from the homepage is less likely to be crawled by AI systems. AEO best practice recommends keeping priority content within 3 levels.

Concern: Only 38.4% of indexable pages sit within 3 clicks of the homepage. Over 30% of content is buried at depth 6 or deeper — these pages are unlikely to be crawled by AI systems. High-value answer content should be elevated closer to the surface.

Schema & Structured Data Coverage

Structured data is a critical trust signal for AI systems. While schema may not be used by all AI platforms yet, it makes content machine-readable and is "an easy way to convert content into structured data that makes it easier for AI search platforms to retrieve and interpret information".

Schema Type Distribution (Indexable Pages)

Schema Type Pages Coverage AEO Importance
BreadcrumbList 3,331 87.5% Strong
Organization 1,854 48.7% Strong
WebSite + SearchAction 1,853 48.6% Strong
Article + Person 1,398 36.7% Strong
WebPage + ReadAction 1,817 47.7% Moderate
Product + Offer 322 8.5% Needs Growth
FAQPage + Question + Answer 239 6.3% Critical Gap
VideoObject 41 1.1% Opportunity
HowTo 0 0% Missing
Speakable 0 0% Missing

Schema Validation Issues

Action needed: While schema errors are minimal (2 pages), 1,021 pages have Rich Result warnings — these reduce eligibility for Google rich snippets and AI Overviews. The 33 Rich Result errors should be prioritised for immediate fixes.

Technical Performance

Fast, crawlable sites are preferred by AI systems. Server-side rendering is important because "many AI crawlers seem to have trouble executing JavaScript".

Excellent: Axiom Wave's technical performance is outstanding. Sub-200ms response times across the site, virtually no slow pages, and zero near-duplicate content. This is a strong foundation for AI crawlability.

AEO-Critical Gaps: What's Missing

Best Practice Status Impact
FAQPage Schema on support/help content 6.3% coverage Directly impacts featured snippet and AI Overview eligibility
HowTo Schema for guides and tutorials 0 pages Misses voice search and step-by-step AI answers
Speakable Schema for voice-ready content 0 pages Invisible to voice assistants like Google Assistant
llms.txt file for AI crawlers Not detected Misses opportunity to guide LLM crawlers
Question-based H2/H3 headings Inconsistent Reduces AI's ability to match content to conversational queries
Answer-first content structure Inconsistent AI needs the answer in the first 1-2 lines under a heading

Priority Matrix: Effort vs. Impact

Recommendations plotted by implementation effort (horizontal) and expected impact on AI visibility (vertical).

⚡ Quick Wins (Low Effort, High Impact)

🚀 Strategic Investments (High Effort, High Impact)

📋 Maintenance Tasks (Low Effort, Moderate Impact)

📊 Long-Term Foundations (High Effort, Moderate Impact)

Top 10 Prioritised Recommendations

# Recommendation Effort Impact Timeline
1 Deploy FAQPage schema across all product, support, and service pages Low Critical 2–4 weeks
2 Create llms.txt file guiding AI crawlers to core content Low High 1 week
3 Fix all Rich Result errors (33 pages) and critical warnings Low High 2 weeks
4 Add missing H1 tags and meta descriptions to 2,000+ pages Medium High 4–6 weeks
5 Build answer-first content hubs around "What is 5G?", "IoT for business", etc. High Critical 8–12 weeks
6 Enrich thin content pages — target top 500 thin pages with highest traffic potential High Critical 12–16 weeks
7 Implement HowTo schema on setup guides, troubleshooting, and comparison content Medium High 4–6 weeks
8 Add Speakable schema to top 200 most-visited pages Medium High 4 weeks
9 Rescue orphan pages — add contextual inlinks to 594 pages Medium High 6–8 weeks
10 Launch AI visibility monitoring to track brand citations across AI engines Medium Critical 4 weeks

Content Strategy for AI Citability

Beyond technical fixes, Axiom Wave needs a content strategy built for AI citation. Research shows that adding relevant statistics and quotations can boost visibility in AI-generated responses by up to 40%.

What Makes Content AI-Citable

Principle Current State Action Required
Answer-first structure — direct answer in first 1–2 lines under heading Inconsistent Rewrite key page intros with declarative summary sentences
Statistics and data points — specific numbers beat vague claims Moderate Embed concrete stats (e.g., "339M mobile customers") throughout content
Question-based headings — match how users ask AI Weak Restructure H2/H3 tags as natural-language questions
E-E-A-T signals — author bios, credentials, citations Moderate Add author attribution to all blog/news content
Topical authority — cover the entire "question tree" Moderate Build content clusters around core telecoms topics
Original research — "State of X" reports earn 10x more citations Some reports Publish quarterly original research with citable data

Implementation Roadmap

Weeks 1–2: Quick Wins Sprint

Create llms.txt file. Fix 33 Rich Result errors. Deploy FAQPage schema to existing FAQ content. Begin H1 tag remediation across top-traffic pages.

Weeks 3–6: Foundation Building

Write missing meta descriptions (1,389 pages). Implement HowTo schema on guides. Add Speakable schema to top 200 pages. Begin content enrichment for highest-traffic thin pages. Set up AI visibility monitoring.

Weeks 7–12: Content Transformation

Build answer-first content hubs for 5G, IoT, broadband, and roaming. Restructure headings as questions. Add FAQ sections to all product pages. Rescue orphan pages with contextual internal links.

Weeks 13–16: Scale & Optimise

Expand thin content enrichment to remaining pages. Develop market-specific content for European and African operations. Launch first "State of Connectivity" original research report. Flatten site architecture for deep pages.

Ongoing: Monitor & Iterate

Track AI citation metrics weekly. Monitor brand mentions across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Iterate content based on which queries surface Axiom Wave. Audit new content against AEO/GEO checklist before publication.

AEO/GEO Content Checklist for the Team

Apply this checklist to every new piece of content before publication:

Checkpoint Details
✅ Answer-first structure Direct answer in the first 1–2 sentences under each heading
✅ Question-based headings H2/H3 tags phrased as natural-language questions where appropriate
✅ Schema markup FAQPage, HowTo, Article, or Speakable schema applied as relevant
✅ Statistics and evidence At least 3 specific data points per 1,000 words
✅ E-E-A-T signals Author bio, publication date, last-updated date, clear sourcing
✅ Internal linking At least 3 contextual internal links to related content
✅ Content depth Minimum 1,000 words for informational content; 500+ for product pages
✅ Readability Flesch Reading Ease score of 60+ (Fairly Easy to Normal range)
✅ Meta description Compelling, 120–160 characters, includes primary answer
✅ Voice-ready Key answers readable aloud in under 30 seconds

Appendix: Sources & References

This assessment draws on the following industry sources, all accessed in March 2026:

Methodology: This assessment was conducted by analysing three website crawl datasets (internal pages, links, structured data) containing 7,599 URLs, combined with manual inspection of the live website and benchmarking against AEO/GEO best practices from 12+ authoritative industry sources. Readiness scores are calculated from weighted metrics across six dimensions.