2.2 Columns: Your Processing Steps

In Section 2.1, you learned that rows are individual records—like each lead or customer in your system. Now let's explore columns, which are the processing steps that make your automation work.

If rows are the heartbeat, columns are the muscles—they do the work. Each column either stores data you enter or automatically processes it. Together, your columns form a pipeline that transforms raw information into valuable results.

The Concept: What Are Columns?

Columns as Processing Steps

A column represents one step in your automation.

Think of it this way:

A row is a lead flowing through your system
Each column is a station that lead passes through
Column 1: Email validation
Column 2: Company data enrichment
Column 3: Lead scoring with AI
Column 4: Send personalized email

The column defines WHAT happens. The row provides the data.

Key insight: Columns are reusable templates. Every row goes through the same columns, but each row gets its own independent execution.

Two Fundamental Types

All columns fall into two categories:

1. Manual Columns (You Enter the Data)

Manual columns store data that you type directly.

Examples:

Email address
Priority flag
Notes and comments
Assigned team member

What they're like:

✅ Editable: You can type, click, or select values
✅ Simple: No automation—just data storage
✅ Fast: Saves instantly
✅ Flexible: Change values anytime

Think of them as: Spreadsheet cells you type into.

2. Calculated Columns (System Does the Work)

Calculated columns are automated—the system runs them for you.

Examples:

Fetch company data from an API
Analyze text with AI to score quality
Generate a personalized email draft
Push data to your CRM

What they're like:

✅ Automated: Runs without you clicking anything
✅ Powerful: Can call APIs, run code, use AI
✅ Smart: Waits for other columns before running
✅ Trackable: Shows status (waiting, running, done, failed)

Think of them as: Automated functions (like Excel formulas, but way more powerful).

How Columns Work Together

Here's how columns create a complete automation:

Step 1: Manual Input

📧

Manual Entry

You type: john@example.com

🏢

Company

Manual Entry

You type: Acme Corp

↓

Step 2: Automated Processing

✓

Email Valid

Auto-Run

System checks: Is email format valid?

↓

🌐

Company Data

API Call

System fetches data from Clearbit

↓

🏭

Industry

Extract

Pulls: "Technology"

👥

Size

Extract

Pulls: "500 employees"

↓

🤖

Lead Score

AI Analysis

AI analyzes all data → Score: 8/10

↓

Step 3: Action

📧

Send Email

Action

System sends personalized outreach

What You See:

Manual Entry

You type the data

Auto-Run

System runs automatically

Action

Final step executes

What happens:

You enter email and company name (manual columns)
System validates the email automatically
If valid, system fetches company data from an API
System extracts specific fields (industry, employee count)
AI analyzes all data and scores the lead
System sends a personalized email based on the score

Each column = one step. Together = complete automation.

Column Properties

Every column has some basic settings:

Property	What It Means	Example
Label	What you see in the column header	"Email Address", "Lead Score"
Name	Internal identifier (auto-generated)	`email_address`, `lead_score`
Type	What kind of column it is	Text, AI Prompt, Email Sender
Configuration	Settings specific to this type	AI model, prompt template, dependencies

Most important distinction:

Manual columns: Simple types (text, number, toggle, etc.)
Calculated columns: Powerful types (AI, API calls, integrations)

Column Types Overview

Let's explore all the types of columns you can create.

Manual Column Types

These are the building blocks for data you enter yourself.

1. Text

What it is: Type text into a field (single line or multiple lines)

Use it for:

Names, emails, addresses
Notes, descriptions, comments
URLs, IDs, codes

Options:

Allow multiple lines
Add placeholder hint text

Example: Contact notes field where you can write anything

2. Number

What it is: Store numeric values

Use it for:

Scores, ratings, quantities
Prices, amounts, percentages
Counts, metrics

Example: Lead score from 1-10

3. Toggle (On/Off Switch)

What it is: True/false value with a switch you can flip

Use it for:

Status flags (active, qualified, contacted)
Yes/no questions (approved, verified, completed)

Example: "Qualified Lead?" toggle

4. Date & Time

What it is: Calendar picker to select dates and times

Use it for:

Deadlines, schedules
Birth dates, event dates
Date ranges (start and end)

Options:

Include time of day
Select a date range
Choose time zone

Example: Follow-up date picker

5. JSON Data

What it is: Store complex structured data

Use it for:

Full API responses
Configuration objects
Nested data structures

Example: Raw API response from an enrichment service

6. Multi-Select

What it is: Choose multiple values from a list

Use it for:

Tags, categories, labels
Skills, technologies, interests

You define: The list of allowed values

Example: Tags like "hot-lead", "qualified", "contacted"

7. User Select

What it is: Choose one or more users from your team

Use it for:

Assign tasks to team members
Track ownership
Collaboration (multiple people)

Example: "Assigned To" dropdown

8. File Upload

What it is: Upload and attach files

Use it for:

Documents, contracts, invoices
Images, photos, screenshots
Spreadsheets, PDFs

Example: Contract document field

Calculated Column Types (by Category)

Calculated columns are organized into categories based on what they do.

Category 1: AI (Artificial Intelligence)

What it does: Use AI and machine learning to process data

Available Types:

AI Prompts: Use GPT-4, Claude, Gemini, and more
- Generate text
- Analyze and classify
- Extract information
- Answer questions
Sentiment Analysis: Detect positive/negative tone
Classification: Categorize text into groups
Knowledge Storage: Store information for AI to reference later

Example:

Column: Lead Analysis
Type: AI Prompt
What it does: "Analyze this lead and rate industry fit 1-10,
              assess company size, and recommend pursue or skip"

Category 2: Data & Storage

What it does: Transform, parse, and manipulate data

Available Types:

Transform Data: Map, filter, reorganize collections
Parse Data: Extract from JSON, XML, HTML
Format Data: Convert between formats
File Parser: Read CSV, Excel, PDF files
Custom Code: Write your own logic

Example:

Column: Clean Phone Number
Type: Custom Code
What it does: Removes all non-numeric characters from phone field

Category 3: Communication

What it does: Send and receive messages

Available Types:

Send Email: Via SMTP or email services
Receive Email: Listen for incoming emails
SMS: Send text messages via Twilio
WhatsApp: Send WhatsApp messages
Slack: Post to channels or send DMs
Discord: Post to Discord servers
Microsoft Teams: Send Teams messages

Example:

Column: Send Outreach Email
Type: Send Email
What it does: Sends personalized email to the contact's email address

Category 4: CRM & Sales

What it does: Connect with CRM systems and sales tools

Available Types:

Salesforce: Create/update leads, contacts, opportunities
HubSpot: Manage contacts, deals, tickets
Pipedrive: Track deals and activities
Clearbit: Company data enrichment
Apollo.io: B2B contact information
Hunter.io: Find and verify email addresses

Example:

Column: Create Salesforce Lead
Type: Salesforce
What it does: Creates a new lead in Salesforce with contact info

Category 5: Development

What it does: Developer tools for APIs and custom integrations

Available Types:

Webhook: Listen for incoming HTTP requests
HTTP Request: Make API calls to any service
Custom Code: Execute code in multiple languages
Web Scraper: Extract data from websites
Browser Automation: Control a headless browser
GraphQL: Execute GraphQL queries

Example:

Column: API Call
Type: HTTP Request
What it does: Calls your custom API to enrich contact data

Category 6: E-commerce

What it does: Manage online stores and payments

Available Types:

Shopify: Create/update products and orders
WooCommerce: Manage WordPress store
Stripe: Process payments and subscriptions
PayPal: Payment processing
Square: POS and payment processing

Example:

Column: Create Shopify Order
Type: Shopify
What it does: Creates order in your Shopify store

Category 7: Marketing

What it does: Marketing automation and campaigns

Available Types:

Mailchimp: Email campaigns and lists
SendGrid: Transactional emails
Facebook Ads: Create and manage ads
Google Ads: Manage ad campaigns
LinkedIn Ads: B2B advertising
Social Media: Post to Twitter/X, Instagram, TikTok

Example:

Column: Add to Mailchimp
Type: Mailchimp
What it does: Adds contact to your email marketing list

Category 8: Productivity

What it does: Calendar, tasks, and productivity tools

Available Types:

Google Calendar: Create/update events
Outlook Calendar: Microsoft calendar management
Google Tasks: Task management
Asana: Project and task management
Trello: Create and update cards
Notion: Database and note management

Example:

Column: Create Calendar Event
Type: Google Calendar
What it does: Schedules a meeting on your calendar

Category 9: Automation

What it does: Control workflow logic and timing

Available Types:

Conditional Logic: If/then/else branching
Wait/Delay: Pause execution for a time period
Schedule: Time-based triggers
Loop/Iterate: Process lists of items
Sub-Taible Operations: Work with nested data

Example:

Column: Wait 24 Hours
Type: Wait/Delay
What it does: Pauses for 24 hours before continuing

Category 10: Utilities

What it does: General-purpose operations

Available Types:

Date/Time Operations: Calculate and format dates
String Manipulation: Combine, split, replace text
Math Calculations: Add, multiply, formulas
Format Conversions: Convert between data formats
Validation: Check email, phone, URL formats
Deduplication: Remove duplicates

Example:

Column: Days Since Contact
Type: Date Calculation
What it does: Calculates days between last contact and today

Category 11: Custom

What it does: Your own custom integrations

Available Types:

Custom Nodes: Your organization's custom types
Plugin Extensions: Third-party integrations
Company-Specific: Custom business logic

Use for: Proprietary systems and unique workflows

Adding a Column

Let's walk through how to add a new column step-by-step.

Step 1: Open Column Selector

Click the "+ Add Column" button in the top-right of your table.

A large selection window opens showing all available column types:

Add Column

Step 2: Filter by Category (Optional)

Click a category in the left sidebar to see only those column types.

For example:

Click "AI" to see only AI-powered columns
Click "Communication" to see email, SMS, and messaging options
Click "All" to see everything again

Step 3: Search (Optional)

Type in the search bar to quickly find what you need.

Examples:

Type email → Shows email-related columns
Type AI → Shows all AI columns
Type enrich → Shows data enrichment tools

Step 4: Select a Column Type

Click on a tile to select that column type.

The selection window closes and a configuration sidebar slides in from the right.

Step 5: Configure the Column

Now you'll set up how this column works. The configuration has three tabs at the top:

🤖

Configure Column

AI Prompt

Column Label

Column Name (auto-generated)

lead_analysis

AI Model

Prompt Template

Advanced Options

Temperature: 0.7

Max Tokens

Tab 1: AI Assistant

What it does: Let AI configure the column for you using plain English.

How to use it:

Describe what you want in natural language
Click "Generate Configuration"
AI sets up everything automatically
Review and approve (or adjust)

Example:

You type: "Analyze leads and score them 1-10 based on company size"

AI configures:
- Model: GPT-4
- Prompt template with scoring instructions
- Dependencies: Waits for company data
- Output: Number from 1-10

Pro tip: This is the easiest way to set up complex columns!

Tab 2: Data Configuration

What it does: Manually configure all the settings for this column.

What you'll set:

Basic Settings (all columns):

Column Label: What users see (e.g., "Lead Score")
Column Name: Auto-generated (e.g., lead_score)
Description: Optional notes about what this column does

Type-Specific Settings (varies by type):

For Manual Columns:

Simple options like "allow multiple lines" for text
Allowed values for multi-select
Time zone for date fields

For Calculated Columns (examples):

AI Prompt:

Choose AI model (GPT-4, Claude, etc.)
Write prompt template
Set temperature and token limits

Email Sender:

Email account to send from
Subject line
Email body template

HTTP Request:

API endpoint URL
Request method (GET, POST, etc.)
Headers and authentication
Request body template

CRM Integration (e.g., Salesforce):

Choose action (Create Lead, Update Contact, etc.)
Map fields to your data
Select connected account

Tab 3: Run Configuration

What it does: Control when and how this column runs.

When should it run?

Run once when ready: Runs once when all dependencies are available (most common)
Re-run when data changes: Runs again if the data it depends on changes
Manual only: You decide when to run it by clicking a button
Scheduled: Runs on a schedule (e.g., daily at 9am)

What does it need first? (Dependencies)

Select which columns must complete before this one runs
Example: "Wait for email and company_name columns"

Should it run conditionally? (Optional)

Add a condition to skip execution in certain cases
Example: "Only run if [Lead Score] is greater than 50"
Shown in plain English like: [Lead Score] > 50

Rate limiting (Optional):

Limit how many times this can run per hour/day
Useful for API integrations with usage limits

Step 6: Save

Click the "Save" button in the top-right of the sidebar.

What happens:

Column is added to your table
Column header appears in the grid
For calculated columns, existing rows start processing automatically (if dependencies are met)

Working with Columns

Once you have columns, here's how to manage them.

Viewing Column Status

In the column header, you'll see:

Running indicator:

Green spinning icon with a number (⟳ 3)
Shows how many cells are currently processing

Queued indicator:

Clock icon with a number badge (🕐 7)
Shows how many cells are waiting to run

Hover over indicators to see:

Which specific rows are running or queued
Click to jump to those rows

Editing a Column

To change settings:

Click the three-dot menu (⋮) in the column header
Select "Configure Column"
Configuration sidebar opens (same as when you added it)
Make your changes
Click "Save"

What you can change:

✅ Label and description
✅ All type-specific settings
✅ Run configuration (dependencies, conditions)
❌ Column type (can't change text to AI, for example)
❌ Column name (can't rename after creation)

Important: Changes affect future executions, not past data. To recalculate with new settings, re-run the column.

Reordering Columns

To rearrange how columns appear in the table:

Click and hold the column header
Drag left or right
Drop where you want it

Note: Visual order doesn't affect execution order—that's controlled by dependencies.

Good practices:

Put manual input columns on the left
Group related processing steps together
Keep final output columns on the right

Click the three-dot menu (⋮) on any column header to see all available operations:

🤖

Lead Analysis

AI Prompt

⟳3

🕐7

Re-run Options

Choose how you want to re-run the column:

Re-run Empty Cells: Only process cells that have no data yet

Use when: Adding new rows or fixing cells that never ran

Re-run Failed Cells: Retry cells that previously failed

Use when: Fixing errors or API issues that are now resolved

Re-run All Cells: Recalculate every cell regardless of status

Use when: You changed configuration and want to recalculate everything

Re-run Cell Range: Process only selected rows

Use when: You want precise control over which rows to process

Clear Options

Reset cells back to empty:

Clear Column: Remove all data from every cell in this column

Use when: Starting over with fresh calculations

Clear Cell Range: Remove data from selected rows only

Use when: Clearing specific entries

What happens: Cells become empty. If downstream columns depend on this one and are set to re-run when data changes, they'll recalculate automatically.

Cancel Options

Stop currently running or queued tasks:

Cancel All Tasks: Stop everything running or waiting in this column

Use when: You need to stop processing (wrong configuration, API issues)

Cancel Task Range: Stop only selected rows

Use when: Stopping specific rows while letting others continue

What happens: Running tasks stop, queued tasks are removed. Cells go back to empty or keep their last successful result.

Adding Columns Adjacent

To insert a new column next to an existing one:

Click the three-dot menu (⋮) on any column
Select "Add Column to Left" or "Add Column to Right"
Column selector opens
New column appears exactly where you wanted it

Use when: Inserting a processing step between existing columns without rearranging everything.

Converting to Sub-Taibles

When you have array/list data in a column (like a list of order items or contacts), you can convert it into a nested table.

To convert:

Click the three-dot menu (⋮) on the column with list data
Select "Convert to Sub-Taible"
Configuration window opens:
- Choose which fields from the objects should become columns
- Set column names
- Select unique ID field (optional)
Click "Convert"

What happens:

System creates a nested table
Original column shows item count (e.g., "5 items")
Click the count to drill down into child rows
Each item becomes a row in the sub-table

Example:

Before: Order Items column contains:
- Widget A (qty: 2, price: $10)
- Widget B (qty: 1, price: $25)

After: Order Items becomes sub-table with columns:
- Product Name
- Quantity
- Price

Use when: API returns lists, you need to process each item individually, or you want to work with hierarchical data.

Deleting Columns

⚠️ Careful! Deletion is permanent.

To delete:

Click the three-dot menu (⋮) on the column
Select "Delete Column"
Confirmation window appears showing:
- What will be deleted
- Other columns that depend on this one (if any)
Type the column name to confirm (for important columns)
Click "Delete"

What gets deleted:

The column definition
All data in this column (across all rows)
Stored results

What stays:

All rows (they just lose this column)
Other columns
Your table

Best practice: Export data first if you might need it later.

Hiding Columns

Hide without deleting (column still runs, just not visible):

To hide:

Click the three-dot menu (⋮)
Select "Hide Column"
Column disappears from view

To show again:

Click "Manage Columns" button in the toolbar
Check boxes for columns to show
Click "Apply"

Use when:

Hiding intermediate processing steps
Showing only final results
Cleaning up the interface for end users

Example: Hide "raw_api_response" but keep "company_name" extracted from it.

Common Column Patterns

Here are proven patterns for organizing your columns.

Pattern 1: Input → Process → Output

Most common structure:

Manual Input:
  → Email
  → Company Name

Automated Processing:
  → Email Validation
  → Company Enrichment
  → Lead Scoring

Action:
  → Send Email
  → Create CRM Record

Why it works: Clear flow, easy to understand, modular.

Pattern 2: Parallel Processing

Multiple things happen at once:

Input:
  → Email

Parallel Enrichment (all run simultaneously):
  → Clearbit Data (API call)
  → Apollo Data (API call)
  → Hunter Data (API call)

Merge:
  → Combined Data (merges all three)

Advantage: Much faster since APIs are called at the same time.

Pattern 3: Conditional Branching

Different paths based on data:

Input:
  → Email

Validation:
  → Email Valid? (checks format)

Path A (if valid):
  → Company Enrichment
  → Lead Score

Path B (if invalid):
  → Send Error Notification

Implementation: Use conditions on Branch A and B columns to control when they run.

Pattern 4: Extract and Transform

API returns complex data, pull out specific fields:

API Call:
  → Company Data (returns full object)

Extraction (multiple columns pulling from the same source):
  → Company Name
  → Industry
  → Employee Count
  → Funding Stage

Why: Simpler columns are easier to use, filter, and display.

Pattern 5: Aggregation from Sub-Taibles

Roll up data from child rows:

Parent Table: Orders
  → Order Total (sums child rows)

Child Table: Order Items
  → Quantity
  → Unit Price
  → Line Total (Quantity × Unit Price)

Parent calculates: Sum of all Line Totals

Summary: Mastering Columns

You now understand columns completely:

✅ Concept: Columns are processing steps in your automation ✅ Two Types: Manual (you enter data) vs. Calculated (system automates) ✅ Manual Types: Text, number, toggle, date, JSON, multi-select, user-select, file upload ✅ Calculated Types: 11 categories with 100+ integrations (AI, CRM, Communication, etc.) ✅ Adding Columns: Category filter + search + click to select ✅ Configuration: 3 tabs (AI assistant, data config, run config) ✅ Operations: Configure, re-run (empty/failed/all), clear, cancel, convert to sub-taible, delete ✅ Common Patterns: Input → Process → Output, parallel processing, conditional logic, extraction

Key Principles:

Columns define WHAT, rows provide the data
Manual = you enter, Calculated = system automates
Configuration has 3 tabs: AI assistant (easiest), data (detailed), run (when/how)
Visual order is flexible, dependencies control execution order
Hide intermediate steps for clean interfaces
Use proven patterns to build faster
Re-run operations give you control (empty, failed, all, or range)
Convert collections to sub-taibles for nested data

Columns are the workhorses of your automation. Master them, and you can build incredibly powerful workflows without writing code.

Next: Section 2.3: Cells - Where Data Meets Processing — Now that you understand rows and columns, let's explore cells—where the actual work happens.

2.2 Columns: Your Processing Steps ​

The Concept: What Are Columns? ​

Columns as Processing Steps ​

Two Fundamental Types ​

1. Manual Columns (You Enter the Data) ​

2. Calculated Columns (System Does the Work) ​

How Columns Work Together ​

Column Properties ​

Column Types Overview ​

Manual Column Types ​

1. Text ​

2. Number ​

3. Toggle (On/Off Switch) ​

4. Date & Time ​

5. JSON Data ​

6. Multi-Select ​

7. User Select ​

8. File Upload ​

Calculated Column Types (by Category) ​

Category 1: AI (Artificial Intelligence) ​

Category 2: Data & Storage ​

Category 3: Communication ​

Category 4: CRM & Sales ​

Category 5: Development ​

Category 6: E-commerce ​

Category 7: Marketing ​

Category 8: Productivity ​

Category 9: Automation ​

Category 10: Utilities ​

Category 11: Custom ​

Adding a Column ​

Step 1: Open Column Selector ​

Add Column

Step 2: Filter by Category (Optional) ​

Step 3: Search (Optional) ​

Step 4: Select a Column Type ​

Step 5: Configure the Column ​

Tab 1: AI Assistant ​

Tab 2: Data Configuration ​

Tab 3: Run Configuration ​

Step 6: Save ​

Working with Columns ​

Viewing Column Status ​

Editing a Column ​

Reordering Columns ​

Column Operations Menu ​

Re-run Options ​

Clear Options ​

Cancel Options ​

Adding Columns Adjacent ​

Converting to Sub-Taibles ​

Deleting Columns ​

Hiding Columns ​

Common Column Patterns ​

Pattern 1: Input → Process → Output ​

Pattern 2: Parallel Processing ​

Pattern 3: Conditional Branching ​

Pattern 4: Extract and Transform ​

Pattern 5: Aggregation from Sub-Taibles ​

Summary: Mastering Columns ​

2.2 Columns: Your Processing Steps

The Concept: What Are Columns?

Columns as Processing Steps

Two Fundamental Types

1. Manual Columns (You Enter the Data)

2. Calculated Columns (System Does the Work)

How Columns Work Together

Column Properties

Column Types Overview

Manual Column Types

1. Text

2. Number

3. Toggle (On/Off Switch)

4. Date & Time

5. JSON Data

6. Multi-Select

7. User Select

8. File Upload

Calculated Column Types (by Category)

Category 1: AI (Artificial Intelligence)

Category 2: Data & Storage

Category 3: Communication

Category 4: CRM & Sales

Category 5: Development

Category 6: E-commerce

Category 7: Marketing

Category 8: Productivity

Category 9: Automation

Category 10: Utilities

Category 11: Custom

Adding a Column

Step 1: Open Column Selector

Step 2: Filter by Category (Optional)

Step 3: Search (Optional)

Step 4: Select a Column Type

Step 5: Configure the Column

Tab 1: AI Assistant

Tab 2: Data Configuration

Tab 3: Run Configuration

Step 6: Save

Working with Columns

Viewing Column Status

Editing a Column

Reordering Columns

Column Operations Menu

Re-run Options

Clear Options

Cancel Options

Adding Columns Adjacent

Converting to Sub-Taibles

Deleting Columns

Hiding Columns

Common Column Patterns

Pattern 1: Input → Process → Output

Pattern 2: Parallel Processing

Pattern 3: Conditional Branching

Pattern 4: Extract and Transform

Pattern 5: Aggregation from Sub-Taibles

Summary: Mastering Columns