Using AI to Populate Tables Automatically
AI Tables can be filled, cleaned, or updated automatically using Frontline’s AI.
This means you don’t need to manually add or edit records — your agents and workflows can generate and maintain data dynamically in real time.
This article explains how AI can create, enrich, and manage your data inside tables.
Populating tables with AI
There are several ways the AI can add or update records in your tables:
1. From conversations
When users interact with your AI agents, the system can automatically extract relevant data and store it in a table.
For example:
A user says, “I’m interested in renting an apartment in Montevideo.”
The AI can automatically create a new record in your Leads or Real Estate table, filling out fields like name, location, intent, and conversation summary.
📸 Screenshot of a Real Estate Leads table automatically filled with user information from chat interactions
2. From workflows
Workflows can automatically create or update table records after specific events occur in your workspace.
Examples include:
When a new support ticket is received, create a record in the Tickets table.
When a form submission is completed, update the Leads table.
When a payment is confirmed, update the Invoices table with payment status.
📸 Screenshot showing the Workflow Builder with a “Create record in table” action selected
3. From imports
You can also upload CSV or XLSX files directly into a table, and let AI detect and map the right fields automatically.
During import, the system uses AI to:
Identify matching columns (e.g. “Email” → Email field).
Detect data types (numbers, text, dates, etc.).
Clean and standardize the data (for example, removing duplicates or fixing capitalization).
📸 Screenshot of the Import Data panel showing automatic field mapping and data preview
AI enrichment and cleaning
AI can also enrich existing records by analyzing data or conversations related to them.
This includes:
Adding summaries or classifications to each record.
Detecting sentiment or intent.
Completing missing contact details when available.
Categorizing entries automatically (e.g. Hot / Warm / Cold leads).
📸 Screenshot of a Leads table enriched by AI with sentiment and classification fields auto-filled
This process runs automatically in the background when new information is detected by your agents or workflows.
Natural language creation
You can also use natural language to ask the AI to create or update data in a table.
For example, simply say:
“Add this client to my Leads table.”
“Create a new invoice for John Doe for $200.”
“Save this conversation as a Support Ticket.”
📸 Screenshot showing an AI chat where a user command adds a new record to a table
The agent interprets your instruction, identifies the correct table, and fills the relevant fields automatically.
Best practices
Use clear and consistent field names so AI can map data accurately.
Add “Select” or “Relation” fields to structure your information and make it easier for AI to organize.
Keep your tables clean — AI will work best when data types are properly defined.
Combine AI population with workflows to create complete automation loops (for example, Lead → Follow-up → Update status).
What’s next
Once your tables are automatically populated, you can:
📸 Screenshot showing connected tables updated in real time by AI and workflows
Tip:
Let the AI do the heavy lifting. Every time your agent interacts with a customer, fills a form, or processes a workflow, it can update your tables automatically — keeping your data always fresh, structured, and ready to use.