Create an AI Analyst
An AI Analyst in Athenic is your workspace to chat with your data and get answers in natural language.
You can create an AI Analyst in two ways:
From the Home page: click "New AI Analyst"
From the Data Sources page: click "Create AI Analyst" (if you have already connected your Data Source)
Set Up your AI Analyst in 3-6 Steps
1. Connect a Data Source
Start by selecting an existing data source or uploading a new one. Athenic supports multiple formats, including CSV uploads and database connections.
For more information, see: Connect a Data Source
2. Create Dataset(s)
After connecting your data source, you’ll create a Dataset—a selection of the relevant tables and fields you want your AI Analyst to understand.
There are two ways to create datasets:
Basic Datasets (no SQL required): Select tables and columns directly
Advanced Datasets (SQL required): Write custom SQL for complex logic
After choosing or writing your datasets, you can:
Adjust field names or data types for clarity
Remove irrelevant or sensitive fields
Select key tables and define relationships
Once you've created your datasets, use the Knowledge Graph’s drag-and-drop editor to:
Define how datasets join together
Embed shared business logic
Link data to business-friendly concepts
For detailed instructions, see Creating Datasets and Data Preparation
3. Add a Dataset to the Knowledge Graph
After saving a dataset:
Click the dataset to add it to the Knowledge Graph
Add context—describe what the dataset contains and how it should be interpreted
Examples: definitions of key fields, business rules, KPI explanations
Define joins—connect this dataset to others to show how they relate
Link shared context—map reusable concepts like “region,” “channel,” or “active user” across datasets
Learn more in Understanding the Knowledge Graph
Optional: Add Suggested Questions
To help users get started quickly, you can add example questions that will appear in the chat window before they type.
These are especially helpful for:
Guiding non-technical users
Demonstrating use cases
Onboarding new team members
Suggested questions will continue to update based on conversation history and usage.
See Suggested Questions for more.
Optional: Define Key Terms
Key terms help guide users as they type questions—especially those less familiar with the data. Admins can define specific business terms (like “churned customers” or “Q1 revenue”) and provide helpful descriptions or context.
These definitions appear in the question builder and serve two purposes:
Improve question clarity by anchoring ambiguous language to known business concepts
Assist users by surfacing common terms they can ask about, along with explanations
This makes it easier for teams to ask precise questions and ensures consistent interpretation across your organization.
Optional: Add Default Question Prefixes
You can optionally set Default Question Prefixes to automatically add filters or context to every question. This is useful when there’s a default business assumption that should apply unless stated otherwise.
Optional: Add Natural Chat Prompt
Guide how the AI interacts with users by setting a custom prompt. Use it to define tone, behavior, or requirements—e.g., “Ask for a date range if not provided,” or “Warn if X data is joined with Y.”
Finalize and Save
Once you’ve completed all steps:
Give your AI Analyst a name
Click Save and return to the Home page, where your new project will appear
You’re now ready to start asking questions, exploring data, and sharing insights.
You will find the Suggested Questions, Key Terms, Default Filters in the Project as followed:
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