AI Tables enable you to extract meaningful insights and make data-driven decisions from unstructured data by providing structured outputs.

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Create a Table

This step requires that you have a source created in your workspace. If you have not ingested your data into a source, see steps on how to do so.

Navigate to “Tables” in the left menu bar, and click on ”+”:

Select the source you wish to creat this table from, then press “Continue”.

Verify that all the data sample is correct and customize the name as you wish:

Press “Create Table”.

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Create an AI Column

To create an AI column, there are two options:

  1. Create a custom column
  2. Use a template

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1. Create a Custom Column

”+ Add column” > Select template: ”+ Blank column” > “Next”

In our example of sales call transcripts, we can create a custom column to indicate whether our call was successful (we define a successful call as one that scheduled a follow-up call):


Column name is the name of your column

Column type is the data type of your column (we use “Checkbox” in this case since success can be a true or false value)

Description is a detailed description of what you wish to extract

Default value is what the value defaults to

Select related columns are the columns that are relevant to extracting this data


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Column Types

We offer various column types, ach tailored to match the specific data type of your output:

  • Text generates free-text outputs a text output, ideal for extracting keywords or phrases without restriction to predefined options
  • Checkbox outputs a boolean (true/false) value, suitable for binary indicators like whether a call was successful
  • Number provides an integer output, useful for counts, such as the frequency a feature is mentioned in a call
  • Currency outputs a floating-point number, perfect for capturing precise values, like prices mentioned
  • List returns a list of strings, ideal for tracking non-standardized items, such as a list of competitor features without a predefined set
  • Select outputs a list of predefined strings, making it easier for standardized analysis, such as listing our own features mentioned during a call (this format is recommended for consistent, analyzable data)

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2. Use a Template

Templates are pre-defined column formats for common use cases, enabling quicker setup and high-quality outputs.

Note: This feature is currently in progress and will be available soon

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Run your AI Table

Upon creating your AI column, hover over the column name and click on

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We get the following options:

We recommend starting with “First 10 rows” to verify if the output meets your expectations, allowing you to make iterations as needed before processing the rest of your dataset.

Once you’re satisfied with the results, proceed to “All rows”.

You also have the option to run individual rows, this is useful for testing multiple AI columns at a time.

With your AI table created, you can run queries on it.