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The AI Hub is an admin space for managing and monitoring how your organization uses Omni’s AI. It brings your AI settings together with adoption, answer quality, and credit usage in one place. From here you can configure AI features and see what people are asking, whether the AI is answering well, and where activity is concentrated.

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Requirements

  • Organization Admin permissions are required to access the AI Hub
  • Your organization must have Omni AI enabled.

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Accessing the AI Hub

Select AI Hub in the sidebar of your Omni instance.

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Configure AI settings

Omni’s AI features are enabled by default. Use these settings to selectively enable or disable features, customize the AI chat interface, choose which LLMs your organization uses, or connect your own LLM provider.

Features

Enable or disable individual AI features.

Global context

Give the Omni Agent Markdown context that applies across your whole organization.

MCP

Enable or disable individual MCP Server settings.

Branding

Customize the appearance of the AI chat interface.

LLM settings

Approve LLMs and set your organization’s default LLM and thinking level.

Bring Your Own Key (BYOK)

Run Omni AI on your own LLM provider key.

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Dashboards

The dashboards in the AI Hub allow you to see how your users are using AI. Use the model selector dropdown in the sidebar to filter the dashboards to a specific shared model. Data populates for any model with AI activity. Every AI session is classified for intent, outcome and sentiment. Models with at least 10 AI sessions in the last 35 days also get a list of question categories, which powers the question analysis in the Quality dashboard. The list is refined as the model passes 20 and 50 sessions. Suggestions require at least one recent chat session with feedback signal. Data refreshes about every two hours, so recent activity appears the same day.

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Improve answer quality

Once you’ve reviewed the Quality dashboard, use these tools to act on what you’ve learned.

Suggestions

Apply prioritized recommendations to improve your model’s AI context, based on user feedback.

Evals

Run prompt sets through your model to score AI accuracy and catch regressions before they reach users.

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Next steps