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GeoContextAI

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GeoContextAI is a Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) platform that helps B2B brands monitor, diagnose, and fix how AI platforms describe them to buyers - segmented across the buyer journey. Most AI visibility tools report a single score across all queries. GeoContextAI is built around a different principle: being mentioned in a discovery query is fundamentally different from winning a head-to-head comparison. The platform segments visibility across three buyer journey stages, each measured differently: - Discovery - "Are you in the conversation?" Brand Presence % across broad category queries like "What's the best CRM for startups?" - Research - "Are you on the shortlist?" Share of Voice across comparison queries like "Compare HubSpot alternatives for sales." - Decision - "Do you win the head-to-head?" Win rate across direct comparison queries like "Should I choose you or CompetitorX?" A brand can have 80% Brand Presence at Discovery and still lose 60% of Decision queries - that's a different problem requiring a different fix. Single-score tools can't see it. THE CLOSED-LOOP WORKFLOW MONITOR - Track brand presence, share of voice, sentiment, and hallucinations across ChatGPT, Perplexity, Gemini, Claude, and DeepSeek. Hallucination detection compares every AI claim against a customer-defined factual baseline, firing alerts when AI states wrong pricing, features, or positioning. DIAGNOSE - Citation forensics trace AI's sources backward to identify why competitors are being cited. Gap classification covers four specific types: technical, specificity, entity, and reputation. Five-signal page diagnosis surfaces what's missing across owned and earned media. FIX - Prioritised recommendations are matched to the specific gap type, with re-scan verification to prove improvement. Before/after deltas show real movement in AI visibility, not vanity metrics. WHO IT'S FOR Built for GEO/AEO professionals, growth marketers, and agencies serving B2B brands. GeoContextAI replaces manual audit work - hours of prompt-testing and screenshot copying across multiple AI platforms with a structured evidence pipeline that produces client-ready output in 15 minutes.

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