[1] Online and LLM Sentiment

Captures the emotional tone and sentiment expressed about your brand across AI-generated responses and public web mentions. Analyzes sentiment polarity (positive, neutral, negative) from both AI system outputs (ChatGPT, Claude, Gemini responses) and web content (news articles, reviews, social media, forums). Combines quantitative sentiment scoring with qualitative examples to reveal how your brand is emotionally perceived across the digital landscape that trains and informs AI systems.

Methodology: Weighted combination of 40% AI response sentiment + 60% web mention sentiment. AI responses are extracted from multi-provider queries, while web mentions are gathered from authoritative sources. Natural language processing assigns sentiment scores (-1 to +1) with distribution analysis across positive/neutral/negative categories.

[2] Competitor Comparison

Reveals how your brand stacks up against direct competitors across key brand attributes in AI understanding. Uses AI systems to compare attribute associations (innovative, creative, welcoming, etc.) between your brand and identified competitors. Calculates attribute dominance scores to measure competitive positioning strength. Identifies specific attributes where you lead or lag, informing strategic differentiation opportunities.

Methodology: Qualitative LLM-based analysis comparing brand vs. competitor attribute associations across multi-provider AI responses. Competitive gap calculations measure attribute dominance percentage, with market positioning classification (leader/challenger/niche/emerging) based on overall competitive strength.

[3] Perception Gaps

Identifies misalignments between your intended brand identity and how AI systems actually perceive and represent your brand. Compares ground truth brand positioning (desired personality traits, tone, values) against actual AI-generated descriptions. Quantifies perception alignment on a 0-100 scale, flagging critical gaps where reality diverges significantly from intention. Reveals which brand attributes are successfully communicated versus which need reinforcement.

Methodology: AI-powered comparative analysis between ground truth brand identity documents and actual brand representations across provider responses. Alignment scoring assesses strength of association for each intended attribute, with gap severity classification (aligned/minor/major/critical) guiding remediation priorities.

[4] Ranking Appearance

Measures your brand's visibility in strategic unbranded search queries where potential audiences might discover you. Analyzes "share of voice" by tracking how frequently and prominently your brand appears in AI responses to relevant but non-branded queries (e.g., "design museums Scotland" vs. "V&A Dundee"). Tracks ranking position, mention quality, and competitive displacement to identify content gaps and visibility opportunities.

Methodology: Simulated query analysis across strategic unbranded searches relevant to brand category, location, and value propositions. Captures brand ranking position, competitor presence, and mention quality across multiple AI providers (ChatGPT, Claude, Gemini). Share of voice calculated as (appearing queries / total queries) with average ranking when present.