Prioritized Measures
In the Full Report you see a list of action recommendations. Each recommendation is assigned to an E-E-A-T principle (Experience, Expertise, Authoritativeness, Trustworthiness) and traceable to concrete findings from the analysis. The recommendations are generated by Flagship models based on the three analysis strands (technical audit, visibility, recognition), taking best practices into account. This multi-stage approach produces better results: the context can be deliberately reset between steps and/or filled with concrete findings, so the AI grounds its recommendations in facts rather than assumptions.Categories
| Category | Focus |
|---|---|
| Technical | llms.txt, robots.txt, Schema, crawlability, structured content |
| Content | Q&A patterns, answer blocks, headings, text clarity |
| Authority | Sources, E-E-A-T signals, external mentions |
Effort Levels
- Quick Win – Quick to implement, direct effect
- Medium – More effort, medium leverage
- Strategic – Long-term measures with high impact
Understanding Priorities
- High: Good leverage – often technical or structural.
- Medium: Content adjustments or new articles.
- Low: Fine-tuning – tackle after the important points.
Typical Recommendations
Technical
- Create llms.txt and fill it with clear statements about brand, offering, and relevant pages
- Check robots.txt – don’t block important AI bots (GPTBot, ClaudeBot, PerplexityBot)
- Schema.org / JSON-LD for Organization, Person, FAQ
- Don’t hide important text in tabs/accordions – AI may overlook it
Content
- Q&A formats and clear answer blocks for AEO/GEO
- Formulate heading and intro text more clearly – make search intent recognizable
- Present comparisons and recommendations in a structured way
Authority
- Strengthen E-E-A-T signals – author bio, verifiable info, sources
- Consistent presentation across all channels – brand, description, expertise
Start with 2–3 high-priority measures. Then you can run another analysis to measure progress.