The Challenge
Enterprise organizations often lack visibility into how their brands are interpreted by non-traditional search systems—specifically Large Language Models (LLMs). The pilot aimed to determine if brand visibility could be quantified across these evolving AI environments.
The Approach
"We didn't use simulated data. We tested our technology on live websites and real brands."
Using the Frontline scoring framework, we analyzed multiple brands within the Elvan Group portfolio. We mapped semantic link structures, entity recognition patterns, and authoritative cross-references that AI models use to build trust.
Outcomes
Key Findings
- Structural clarity was found to matter more than content volume.
- AI visibility varies significantly even between sister brands.
- Small technical adjustments led to measurable visibility gains.
Note: This is a pilot validation, not a marketing testimonial. All outcomes were generated through Frontline’s scoring framework.
