Skip to main content

Understanding Sona AI Chat

Learn how Sona AI Chat lets you ask plain-English questions about your revenue, attribution, account, and pipeline data, and get grounded answers.

Overview

Sona AI Chat, also called Ask Your Data, is a natural language assistant built into the platform. Instead of building a report or writing a query, you ask a question in plain English and Sona AI answers using your connected data. It is the fastest way to interrogate your revenue, attribution, account, and pipeline data, and every answer is grounded in a data source you choose, so you stay in control of what the AI is reasoning over.

New to the chat screen? Start with Getting Started with the New Chat Screen, which covers the New Chat landing screen: the chat input, connecting your data, and the starter templates. This article picks up from there.

Anatomy of the chat screen

The screen has three zones:

1. Chat rail (left): manage your conversations.

  • New Chat: start a fresh conversation.

  • Search Chat: find a past conversation.

  • Playbooks: run a pre-built analysis template instead of writing a prompt from scratch.

2. Data context (centre): this is what makes Sona AI's answers specific to your data.

  • Model selector (top): choose which AI model answers, for example GPT-4.1 mini. Other models are available from the dropdown.

  • Analysis type: the dataset the chat analyzes, for example Revenue Attribution. This is the same selector you set on the New Chat screen; see the Getting Started article for the full list of analysis types.

  • Attribution model: for analysis types that support it, such as Revenue Attribution, a second selector sets the attribution model, for example First Touch Credit, which changes how credit and values are assigned.

  • Upload files: bring your own file, such as a CSV, into the conversation to analyze alongside your platform data.

  • Data preview: a live preview of the selected data so you can confirm you are pointed at the right thing before you ask. The preview shows the first 25 rows; use the row selector and pagination to scan more.

3. Conversation (right): your messages and Sona AI's responses. Type in the box at the bottom: Enter sends, Shift+Enter adds a new line.

Setting your data context before you ask

Sona AI answers within the context you select, so set it first:

  1. Pick an analysis type, for example Revenue Attribution.

  2. If the analysis type supports it, pick the attribution model, for example First Touch Credit. This determines how credit and values are assigned in the answers.

  3. Confirm the data preview below shows the data you expect.

Because answers are scoped this way, the same question can return different results under a different lens. For example, asked which model it is using, Sona AI will respond based on the attribution model you have selected. First touch credit attributes revenue and conversions to the first marketing touchpoint a customer interacted with.

Choosing the AI model

The model selector at the top lets you choose the underlying AI model that generates answers. Models differ in speed and depth, so if an answer feels too shallow or too slow, switching models is the first thing to try.

Uploading files


​Use Upload files to bring your own data into the conversation. This is useful when the data you want to analyze is not already in a Sona data source: upload it, then ask Sona AI questions about it the same way.

Playbooks​

Playbooks are pre-built analysis workflows you open from the left rail, a fast way to run a common analysis without phrasing the prompt yourself. These are different from the starter template cards on the New Chat screen, which are covered in Getting Started with the New Chat Screen. Open Playbooks, pick one that matches what you are trying to learn, and run it against your selected analysis type.

Managing your chats

  • New Chat clears the context and starts over. Use it when you switch topics, so earlier questions do not color new answers.

  • Search Chat lets you find and return to a previous conversation.

Tips for better answers

  • Set the analysis type, and attribution model if shown, first: the answer is only as relevant as the context it is grounded in.

  • Ask one thing at a time: narrow questions get sharper answers than broad ones.

  • Be specific about time frames and segments: for example, "by marketing channel, last quarter".

  • Check the preview if an answer looks off: you may be pointed at the wrong analysis type or attribution model.

Sona AI can make mistakes. Responses are AI-generated. Verify important numbers against the underlying report before you act on them.

FAQs

What is Sona AI Chat?

Sona AI Chat, also called Ask Your Data, is a natural language assistant built into Sona. You ask a question in plain English and Sona AI answers using the data source you select, covering revenue, attribution, account, and pipeline data, without you needing to build a report or write a query.

Why do I need to set an analysis type before asking a question?

Sona AI answers within the data context you select, so the analysis type, and attribution model if applicable, determines what data the answer is grounded in. The same question can return different results depending on the analysis type or attribution model selected, so setting context first ensures the answer is relevant.

What is the difference between Playbooks and the New Chat templates?

Playbooks are pre-built analysis workflows you open from the chat rail on the left, useful once you are already in a conversation. The starter template cards live on the New Chat landing screen and are covered in the Getting Started with the New Chat Screen article. Both let you run a common analysis without writing a prompt from scratch.

Can I trust Sona AI's answers without checking them?

Sona AI's responses are AI-generated and can make mistakes. Verify important numbers against the underlying report before you act on them, and check the data preview if an answer looks off, since you may be pointed at the wrong analysis type or attribution model.

Did this answer your question?