GEO report · public sharing
teliacompany.com
Technical AI readiness measured using Geoa’s free GEO-test — 15 dimensions that determine whether ChatGPT, Claude, Gemini and Perplexity can read, understand and quote the site.
Last scanned: 2026-10-09 · Back to the benchmark
47.7
/100
Grade: F
Low technical readiness for AI — there are significant barriers to AI visibility. Dimensions marked ‘not measured’ are treated as neutral, typically because the site blocks automatic measurements.
Low technical readiness for AI — there are significant barriers to AI visibility. Dimensions marked ‘not measured’ are treated as neutral, typically because the site blocks automatic measurements.
Your Google visibility
52/100
Grade F
Your AI visibility
46/100
Grade F
The 15 dimensions
🤖 AI Crawler Accessibility
100
No issues found ✓
🖥️ Server-Side Rendering
30
- Empty root div — likely client-side rendered (JS required)
📄 llms.txt
0
- No /llms.txt found — strongly recommended for AI visibility. Call POST /llms-txt/generate/{customer_id} to generate one automatically.
- Ingen /llms-full.txt — anbefalet supplement til llms.txt med fuld sideindholdsindeks. llms-full.txt øger citering på Claude og ChatGPT ved at give dem adgang til komplet kontekst.
📊 Structured Data
0
- No JSON-LD structured data found
📝 Basic Page Signals
45
- No H1 heading found — H1 is critical for AI entity recognition
- No canonical tag found — tells search engines and AI which version of the page is the right one
- Manglende Open Graph / Social Sharing tags — AI-crawlere (Perplexity, ChatGPT plugins) bruger og:title og og:description til entitetsgenkendelse og uddrag. Mangler: Mangler og:title meta-tag, Mangler og:description meta-tag, Mangler og:image meta-tag, Mangler twitter:card/twitter:title meta-tags
💬 Content Citability
35
- Low factual density — AI more often cites pages with numbers, prices, years
- BLUF (Bottom Line Up Front) missing — the key point should appear in the first 60 words
- No H2/H3 headings found — hard for AI to extract structure
- No tables or lists — structured content improves AI extraction
🏷️ Brand Entity
50
- No Wikipedia page — a strong signal for AI authority
- No Wikidata entry — less entity recognition in AI
- No LinkedIn link on the website
🔒 Security
100
- Security-headers kunne ikke måles (HTTP 301, bot-beskyttelse) — ikke medregnet
⚡ Performance
58
- CLS 0.46 (denne side, rigtige brugere) — for høj; Googles grænse er 0.1 — på vej ned fra 0.73 over 25 uger, så det virker
- LCP 3637ms (denne side, rigtige brugere) — for langsom; Googles grænse er 2500ms
📱 Mobile Adaptation
100
No issues found ✓
🔗 Internal Links
20
- Only 0 internal links — recommend 5+ for AI crawlability
⭐ E-E-A-T Content Quality
0
- Weak E-E-A-T profile — AI search engines rate trustworthiness as low
- Few trust signals — add author bios, certifications, case studies
🎯 Platform Optimization
65
- Ingen FAQ-/spørgsmålssektion fundet på forsiden — de citerede sider besvarer typisk kundernes spørgsmål direkte i teksten
- Few question-answer patterns — AI often cites direct answers
- Only 0/2 community validation signals found — recommend Trustpilot, G2, case studies or LinkedIn
🗂️ Schema Recommendations
30
- Missing Organization/LocalBusiness schema — most important for AI entity recognition
- Missing WebSite schema — helps AI identify the website's role
- Missing FAQPage schema — FAQs are frequently cited in AI answers
- Missing BreadcrumbList schema — structures the page path for AI
🔢 Empirical Content Data
55
- Lav empirisk tæthed: kun 0 af 1 sætning indeholder konkrete tal, procenter eller kvantificerede resultater. At tilføje statistikker var blandt de mest effektive ændringer i KDD'24-studiet (30-40 % på studiets primære synlighedsmål)
- Intet konkret case-resultat fundet (fx 'sagsbehandlingstid fra 3 uger til 4 dage'). Kundehistorier med målbare resultater er stærkt citerbart indhold — brug kun tal, I selv har målt, og skriv gerne hvornår
- Ingen ekstern kildehenvisning fundet. Citater fra analysefirmaer eller branchestudier øger AI's tillid markant
- Lav teknisk forklaringsdybde (0 sætninger forklarer mekanismen bag jeres løsning). AI prioriterer indhold der forklarer HVORDAN frem for HVAD ('vi bruger RAG + vektor-søgning til ...')
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