LLM Metrix, Inc.

LLM Metrix, Inc. — A synthetic-panel corpus of AI shopping answers: scheduled product and category buying prompts (e.g. 'best X for Y') run daily across 7 AI engines (ChatGPT, Perplexity, Gemini, Claude, Grok, Meta AI, DeepSeek + Google AI Overviews), with the full answer retained and scored — which brands are named, at what position in the answer, with what sentiment, and which exact URLs the engine cited. Repeat-scan cadence makes this a daily-resolution panel of AI product-recommendation behaviour per tracked category, including category share-of-voice time series (brand-vs-competitor ment…

LLM Metrix, Inc. offers (Alternative, Sentiment) — Per account: tracked product/category query set × 7 engines × 365 daily scans, each retaining a full scored answer. A brand tracking ~500 buying queries generates ~1.3M scored answers/year; across an estimated tens-to-low-hundreds of accounts that is low-hundreds-of-millions of scan records lifetime. Exact corpus size needs vendor confirmation — the tracked-queries-per-account distribution is unknown..

Purchase-intent prompt corpus with per-engine recommendation ground truth — training/eval data for shopping-recommendation and answer-engine ranking models; pairwise brand-preference labels across engines for preference modelling; the citation-domain leaderboards (which review sites, wikis and forums engines actually cite for buying questions) as ground truth for content-licensing value assessment — directly useful to publishers and AI providers negotiating content deals; daily-resolution market-share time series usable as an alternative signal for brand momentum (which brand is gaining AI shelf space week over week, ahead of sales data); hallucination/brand-safety error corpus for factuality and safety model evaluation.

The data is with 2 years of history.

Coverage spans US; Application Software; alternative, sentiment; equities.