Case Study 001
Improving Wayli's AI First Impression
84 → 88 → 96 → 100
Before asking businesses to trust AI Readiness, we used it to improve our own website.
This case study documents every assessment, every improvement and every measurable change.
The purpose is not to demonstrate how to achieve a perfect score. It is to show how clearer communication helps AI build a more accurate understanding of a business.
- Business
- Wayli
- Industry
- Decision support platform
- Assessment period
- 10 July – 29 July 2026
- Assessments completed
- 4
- Overall improvement
- 84 → 100 (+16 points)
- 84
- 88
- 96
- 100
The research method
- Assessment
- Finding
- Research
- Implementation
- Reassessment
- Learning
Assessment One
10 July 2026
84 / 100
Finding
Wayli's identity, services and audience were recognised, but the assessment found an important gap in the public evidence supporting trust and authority.
Research undertaken
Rather than making assumptions, we investigated the finding before changing the website or assessment.
Changes implemented
- Expanded the founder story so the people and experience behind Wayli were easier to establish.
- Launched Wayli Labs and began documenting the research behind the product in public.
- Reworked the description of Wayli and strengthened the public evidence explaining what it is and why it exists.
Result
The next assessment recognised the stronger trust evidence while retaining its clear understanding of the business, services and audience.
84 → 88
Assessment Two
15 July 2026
88 / 100
Finding
The original trust gap had been resolved. The remaining work was about reducing uncertainty: making Wayli's purpose, product relationships and public research easier to understand.
Research undertaken
Rather than making assumptions, we investigated the finding before changing the website or assessment.
Changes implemented
- Clarified who Wayli is for and what Wayli does.
- Strengthened the relationships between Wayli's products and made their language more consistent.
- Published more research, improved public authority signals and added an llms.txt file to make the business easier for AI systems to identify.
Result
The following assessment found stronger, more consistent evidence across the site. The report moved from identifying a weakness to isolating one final marginal opportunity.
88 → 96
Assessment Three
21 July 2026
96 / 100
Finding
Wayli's public evidence was consistently strong. A methodology audit then revealed that the Offer assessment could only return 80 of its intended 100 points, making a perfect overall result mathematically unreachable.
Research undertaken
Rather than making assumptions, we investigated the finding before changing the website or assessment.
Changes implemented
- Traced the Offer evidence through the deterministic assessment pipeline.
- Corrected the implementation so all five evidence signals already defined by the methodology could be returned to the scoring logic.
- Reran 99 historical assessments and 29 live benchmark websites to check that no other assessment area moved.
Result
The corrected implementation recognised the evidence that had previously been omitted. This final four-point change was an assessment correction, not a further change to Wayli's website.
96 → 100
Assessment Four
29 July 2026
100 / 100
Finding
The assessment recognised Wayli clearly across every published category. No structural weakness remained within the assessment framework.
Research undertaken
This was the final assessment in the documented period, so there is no subsequent research interval to attribute.
Changes implemented
No later implementation is claimed within this case study.
Result
This assessment closes the documented journey. No later research or implementation is attributed to this result.
What changed?
| Assessment area | 84 | 88 | 96 | 100 |
|---|---|---|---|---|
| Business recognised | ✓ | ✓ | ✓ | ✓ |
| Services understood | ✓ | ✓ | ✓ | ✓ |
| Audience identified | ✓ | ✓ | ✓ | ✓ |
| Trust signals | △ | ✓ | ✓ | ✓ |
| AI visibility | ✓ | ✓ | ✓ | ✓ |
What this demonstrates
AI Readiness was developed through iterative evidence.
Each recommendation was identified, researched, implemented, reassessed and documented.
The improvement from 84 to 96 followed clearer public evidence: a stronger founder story, published research, clearer product relationships, consistent language, improved authority signals and an llms.txt file.
The final change from 96 to 100 came from correcting a verified implementation ceiling, documented in Lab 28: The Day Giving 100% Became Possible. The scoring rules were not relaxed; the correction allowed the engine to apply the published methodology as intended.