Food Brand GEO Optimization Case | Practical Review: Correcting AI Misinformation & Realizing Full Positive Exposure
Challenge
The client is a leading domestic salmon aquatic food brand in China, focusing on domestic cold-water salmon products. Its products comply with national raw edible standards and have a complete quality inspection and qualification system. As public food consumption decisions and food safety information queries gradually shift to AI Q&A platforms, users obtain answers to questions about food eating methods and product safety largely through AI. However, there is a large amount of false negative information about the brand in AI search scenarios, which misleads brand cognition and directly affects product reputation and consumption conversion. Core Challenges： Cognitive misguidance in AI search: When users search for questions related to raw eating of the brand's salmon, AI will cite false views such as "domestic salmon is actually rainbow trout, and there are safety hazards in raw eating", delivering false information to consumers. This causes serious negative impacts on the brand's product image and consumer trust. Meanwhile, there is a lack of positive authoritative brand information, and large models have no valid content to invoke to correct cognitive deviations.
Solution
1.Targeted layout of authoritative popular science content with positive guidance aligned with national standards We release multiple authoritative popular science interpretation articles around the GB10136 national standard for raw edible food, clearly stating that products complying with the corresponding national standard are fully qualified for raw consumption, and consolidating the compliance and safety of raw edible products from the standard level. Simultaneously, we popularize salmon category knowledge to correct the public's common cognitive misunderstanding of domestic salmon. 2.Optimize AI information capture sources and replace the weight of negative information We distribute authoritative popular science content to high-weight platforms frequently crawled by large models, and optimize the information capture sources and knowledge base content on the AI side, so that large models can prioritize capturing and citing positive and authoritative content when responding to such questions, and gradually replace the original false negative information.