Stout assisted a global financial information and market data provider in evaluating whether leading large language models (LLMs) reproduced, summarized, or otherwise reflected proprietary financial news and market content. The engagement involved designing a structured analytical framework to test outputs generated by several leading AI platforms, including OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, xAI’s Grok, and Perplexity’s Sonar Pro, among others, using article-specific prompts, numerical markers, and comparative textual analyses.

In addition to evaluating LLM outputs, we conducted a comprehensive investigation into the potential redistribution of proprietary financial content across publicly accessible websites, financial news aggregators, brokerage platforms, and AI-powered search tools. The analysis included identifying third-party sources carrying similar content, evaluating publication timing, reviewing attribution practices, and assessing potential downstream exposure of proprietary financial information.

We developed automated testing methodologies, comparative analytics, and supporting documentation to help the client evaluate potential content reproduction, redistribution pathways, and AI-related risks for internal investigative and legal purposes.