Context
Designed and structured an AI-powered scoring system to evaluate how brands are perceived and represented across large language models.
The objective was to measure qualitative brand presence within LLM-generated responses; transforming subjective perception into structured insight.
Problem facing
As LLMs increasingly influence information access and decision-making, brand visibility within AI-generated outputs becomes strategically critical.
The challenge was to:
Capture brand representation across prompt variations
Evaluate sentiment, positioning, and authority signals
Structure outputs into measurable scoring dimensions
Scoring architecture
Developed a modular evaluation framework combining:
Prompt scenario variations
Brand mention frequency
Sentiment & contextual positioning
Comparative ranking across competitors
Ensured repeatability and methodological consistency across test runs.
AI infrastructure
Built using GPT-based models to simulate real-world user queries and extract structured scoring data.
The system leveraged AI not only as the object of analysis, but also as the analytical engine.
Experimental layer
The project functioned as a research and experimentation framework, enabling:
Brand monitoring in AI-driven environments
Competitive benchmarking
Scenario testing for strategic positioning
Designed as a foundation for future AI-native analytics products.
Impact
Transformed qualitative AI outputs into measurable brand indicators
Created a replicable evaluation methodology
Positioned AI visibility as a strategic brand metric
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