​
Introducing AI data workflows

The power of Fabi.ai doesn’t just lie in your ability to conduct advanced data analysis and build reports incredibly fast. We also offer the most powerful way to build and automate data analysis and data science pipelines. Here are some of the things you can do with our AI workflow automation:
  • Schedule and process data from SQL databases or Google Sheets using Python and AI
  • Send dynamic, context-relevant insights to Slack
  • Automate data cleaning with SQL, Python and AI
The possibilities are endless.
workflows

​
Fabi.ai workflows 101

​
Cells (nodes)

The concept of cells and nodes are interchangeable. A cell is simply a building block in your workflow, and a Smartbook for that matter. Some of our most popular cells include:
python_cell
These cells can generally do one of three things:
  1. Pull data from a source (examples: SQL, Google Sheets)
  2. Process data (examples: SQL, Python, AI agent)
  3. Send data to a destination (examples: Google Sheets, Email, Slack)

​
Edges

Cells are connected using Python variables and objects. Let’s take a simple example. Say you want to pull data using SQL and process it using Python.
  1. Create a SQL cell and query your data. Your data will automatically be stored in a DataFrame with the name displayed at the bottom of the cell
  2. Create a Python cell that references that DataFrame. This automatically connects those two cells and creates an Edges
This logic can be extended to all cell types and different variables and objects such as strings, DataFrames and images.
Important: In Python cells you can simply refers to variables and objects directly using the name of the variable or object. In all other cells you need to indicate that these are variables using {{your_variable_name}}.
Here’s an example of this in action in an email cell:
python_dataframe