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The AI Insights Agent is a conversational assistant built directly into your experiment results. Instead of manually scanning tables and charts, you can ask plain-language questions and receive data-driven answers grounded in your specific experiment and execution results. The agent is context-aware: it knows which experiment and execution you are viewing, so your questions do not need to include IDs or technical identifiers.

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Accessing the Insights Agent

Open any experiment from the Experiments dashboard, then navigate to its results view. The Insights Agent chat panel appears alongside your results data. You can open a conversation at any time while reviewing an execution.
The agent operates within the scope of the experiment and execution you are currently viewing. It does not search the general web or access data outside your XLR8.ai account.

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Asking questions

Type your question in the chat input and press Enter or click Send. The agent responds with an analysis grounded in your experiment data. You can ask follow-up questions to dig deeper — the agent maintains context across the conversation.

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Example questions

The following questions reflect common AEO analysis workflows. Use them as a starting point or inspiration for your own queries.
  • “Which model mentions us most frequently?”
  • “Which models never cite us in this execution?”
  • “How does our mention rate compare across GPT-4o, Perplexity, and Gemini?”
  • “Which query categories have the lowest mention rate?”
  • “How does our visibility compare to Competitor X in this execution?”
  • “Which models favor our competitors over us?”
  • “Where are we beating Competitor X, and where are we losing?”
  • “What are the main reasons we’re not being cited?”
  • “Which queries consistently fail to mention us?”
  • “Are there query categories where we have zero visibility?”
  • “What is the overall sentiment toward our brand in this execution?”
  • “Which models describe us most positively?”
  • “Are there any negative themes in how we are being mentioned?”

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Conversation flow

The agent maintains the full history of your conversation within the current session. You can build on previous answers to drill down into specific findings.
1

Start with a broad question

Open with a high-level question, such as “Summarize our visibility results for this execution.”
2

Follow up on specifics

Ask follow-up questions to investigate particular models, categories, or competitors: “Why is our mention rate low on Perplexity specifically?”
3

Request actionable takeaways

Close the conversation with an action-oriented question: “What should we focus on to improve our visibility in the next execution?”

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Tips for useful answers

Be specific about what you want to compare. Questions like “Compare our visibility on GPT-4o vs. Gemini for the Healthcare category” produce more focused answers than “How are we doing?”
If you are viewing a specific execution, the agent focuses on that execution’s data. Switch to a different execution in the results view before asking execution-specific questions.
Ask the agent to summarize before you dig into details. A summary response often reveals the most important finding and guides your next question.

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Limitations

The Insights Agent answers questions based on your experiment data only. It cannot browse the web, fetch live search results, or access data from outside your XLR8.ai account.
  • Conversations are session-scoped. Starting a new session clears the conversation history.
  • The agent focuses on the experiment and execution you are currently viewing. If you need to compare across multiple experiments, navigate to each one separately.
  • Answers reflect the data captured at the time the execution ran. The agent does not have access to real-time AI model outputs.

Experiments overview

Learn how experiments are structured and how executions are run.

Visibility experiments

Understand how visibility data is collected across AI models.

Sentiment experiments

See how sentiment is measured and scored per execution.

Action Center

Turn insights into prioritized recommendations.