AiPlex ORM – Frequently Asked Questions (FAQs)
Answers on ORM, negative results & identity safety
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Frequently Asked Questions
We test your brand across ChatGPT, Gemini, Copilot, Perplexity, and Bing AI, analysing consistency, sentiment, data accuracy, citation patterns, and model behaviour.
Yes. AI engines may invent details or misinterpret context, which can mislead users and create negative perception. Managing AI training signals reduces such risks.
Bias usually comes from outdated content, incomplete information, low-authority data sources, conflicting signals, or reputational noise that LLMs pick up during generation.
Standard ORM handles search engines and social platforms. AI perception management focuses on AI-generated narratives, ensuring models don’t misinterpret or distort your brand.
Yes. By strengthening authoritative data sources and optimizing knowledge graph signals, we shape the information LLMs rely on when generating summaries.
AI models aggregate information from web pages, structured data, citations, credible news, social signals, and widely referenced sources. Enhancing these signals improves brand representation.
Yes. You receive detailed reports including ranking positions, sentiment shifts, visibility score, and suppression progress.
Yes — AiPlex supports agencies with white-label SERP monitoring, suppression, and content solutions.
Yes — outdated content is one of the easiest to suppress using fresh, optimized, and high-authority positive assets.
Our team provides rapid response protocols within hours, including high-speed content publishing and SEO reinforcement.
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