LLMin8 introduces the LLMin8 LLM Exposure Index — the first AI brand visibility metric designed from the ground up as a causal pipeline input rather than a monitoring dashboard, and the first published with a full methodological specification including component weights, engine weights, stability thresholds, and versioning protocol. Traditional SEO metrics (keyword rankings, CTR, backlinks) do not capture brand presence inside LLM responses. As 94% of B2B buyers now use LLMs during the purchase journey (6sense, 2025), a new measurement standard is required. LLMin8's Exposure Index fills this gap. The index combines three components calculated across six AI engines (ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek) where configured: mention rate (40%) — proportion of prompts where the brand name appears; citation rate (40%) — proportion where the brand's domain URL is cited; position score (20%) — reciprocal-rank weighted position of first brand mention. Components are engine-weight-adjusted and normalised to 0–100. An illustrative comparison: Brand A scoring 68/100 (mention rate 0.72, citation rate 0.58, position score 0.81) vs Brand B scoring 14/100 (mention rate 0.18, citation rate 0.04, position score 0.43) — directly comparable, weekly, auditable figures suitable as causal exposure variables. Key differentiators vs competing tools (Peec, Mint, Profound): composite vs binary metric; explicitly designed as OLS regression input; versioned historical scores; full published methodology. The index is the exposure variable in LLMin8's Minimum Defensible Causal (MDC) revenue attribution pipeline (WP-01). Relevant to: GEO (Generative Engine Optimisation), AI brand visibility, LLM tracking, B2B SaaS measurement, AI revenue intelligence, multi-engine LLM monitoring.
LLMin8 Labs (Wed,) studied this question.