The ERGO Innovation Lab and ECODYNAMICS teamed up to analyze how insurance content shows up in AI-powered search. Their study looked at over 33,000 AI search results and 600 websites, focusing on which types of content large language models like ChatGPT tend to surface. The results show that LLMs favor content that is easy to read, well-structured, and trustworthy - all traits associated with classic SEO. Modular content, presented in a question-and-answer style, and well-linked internally is also more likely to show up in AI-generated answers, just as in classic SEO.
The study also looked at hallucination rates. ChatGPT had the highest rate, with just under ten percent of its responses containing inaccuracies, while you.com delivered much more reliable results. These findings apply specifically to insurance-related queries.
OpenAI is demonstrating how deep research agents can automate complex research tasks. These agents use the recently released o3-deep-research-2025-06-26 model through OpenAI's API, as well as web search and internal document search with the MCP system. The typical workflow involves four specialized agents: Triage, Clarification, Instruction, and Research. When a user submits a query, the system first checks and, if needed, clarifies it. Then, an instruction agent builds a structured research request, which the research agent carries out.
For simpler tasks, OpenAI offers a lightweight single agent powered by the o4-mini model. This setup is intended for developers looking to create scalable research workflows with OpenAI's tools.
