OranGEO

As AI-powered search and answer engines become primary discovery channels for consumers and business buyers, OranGEO addresses the emerging need for a dedicated analytics layer that measures and optimizes brand visibility within AI responses a dimension of digital presence that traditional SEO tools are not designed to track or improve.

The platform's core capability is real-time monitoring of brand citations across all major large language models tracking how frequently, in what context, and with what sentiment each AI system mentions, recommends, or references a business when users ask relevant questions.

OranGEO's proprietary PSOS™ (Prompt Search Optimization Score) algorithm aggregates visibility, citation rates, and recommendation weight across connected AI platforms into a single, quantifiable benchmark of brand authority in the generative AI landscape giving marketing teams a definitive metric for their AI presence comparable to how domain authority serves as a benchmark for traditional SEO.

The platform provides access to actual AI conversation logs real examples of how ChatGPT, Gemini, Grok, and DeepSeek are responding to relevant user queries allowing businesses to see exactly how their brand is being recommended, described, or omitted in AI-generated answers.

This direct window into AI recommendation behavior reveals specific gaps, mischaracterizations, and opportunities that businesses can address through targeted content and optimization strategies rather than guessing at how AI systems are representing them to potential customers.

OranGEO traces the knowledge origins of AI brand perceptions identifying which web sources, publications, review platforms, and content types are most influencing how AI systems understand and represent each business.

This source attribution intelligence allows marketing teams to prioritize the specific content assets and external citations that will have the greatest impact on improving AI brand representation, rather than pursuing broad content creation without understanding which inputs the models weight most heavily in their recommendation logic.