Build an AI-Powered Customer Feedback Analysis System using n8n, Jotform, Airtable, and OpenAI GPT
Introduction & What This Automation Does
In this project, we will walk through a Customer Feedback Analysis System built using n8n, Jotform, Airtable, and OpenAI. The goal of this automation is simple but extremely powerful: turn raw customer feedback into clear, actionable insights automatically.
Many companies collect feedback through forms, but the process of manually reading every message, understanding the sentiment, and deciding what action to take can be slow and inefficient. Important feedback can easily get overlooked, especially when teams receive large volumes of responses.
This automation solves that problem by introducing AI into the workflow.
Instead of manually reviewing each feedback entry, the system automatically analyzes the message using AI. It evaluates multiple aspects of the feedback, including:
Sentiment (positive or negative)
Category of the feedback
Urgency level
A short summary of the feedback
Priority score
Confidence score
Root cause of the issue
Recommended recovery direction
A recovery message that can be sent to the customer
All of these insights are generated automatically using OpenAI and stored in Airtable alongside the original feedback.
This allows teams to quickly understand customer sentiment and prioritize the most important issues without manually reading every message.
The automation also ensures that the right people are notified immediately. Depending on the sentiment of the feedback, the system automatically sends notifications through email and Slack, ensuring that the operational team can respond quickly when needed.
In this project, Airtable is used as the main data storage. All raw feedback and AI-generated insights are stored there so the team can easily review and analyze the results.
https://airtable.com/app2B5gVgIrG2Sw89/shrKBDfJ5ggoAnp85
Inside the Airtable base, two main tables are used.
The Customer Feedback table stores all raw feedback data submitted by customers. This includes the customer's first name, last name, email address, the service related to the feedback, the feedback message itself, and the rating from one to five.
The AI Analysis table stores the AI-generated insights. These include sentiment, category, urgency level, summary, priority score, confidence score, root cause, recovery direction, and recovery message.
By structuring the data this way, the team can easily review both the original feedback and the AI-generated insights.
If you want to build this automation yourself, you can download the complete n8n workflow JSON from the link below and import it directly into your n8n instance.
https://drive.google.com/drive/folders/1cot945mUUJTYL2Kv86RffznmismtcHB4?usp=sharing
Since the full workflow is provided, you can explore each node, study how the workflow is built, modify the logic, and adapt the system to your own use cases.
Before diving into the workflow explanation, let’s first take a look at the quick demo of how the system works.
Quick Demo
You can watch a quick demo of the entire project below to see the automation working from start to finish.
https://www.youtube.com/watch?v=l8XI_fTD-Ic
Technologies Used and Prerequisites
Before building this automation, make sure you have access to the following tools and services.
Technologies Used
n8n (workflow automation platform)
Jotform (customer feedback form)
Airtable (data storage)
OpenAI GPT model (AI feedback analysis)
Slack (team notifications)
Email service (for sending automated responses)
Prerequisites
To follow this project, you should have:
An n8n instance running (self-hosted or cloud)
A Jotform account with a feedback form created
An Airtable base for storing feedback and analysis
An OpenAI API key
A Slack workspace with a channel for notifications
An email account configured in n8n
Once these services are connected inside n8n, the workflow can automatically connect all systems together.
Workflow Explanation
This automation is powered by a single main workflow called Customer Feedback Analysis Main Workflow. This workflow connects Jotform, Airtable, OpenAI, Slack, and email into one automated pipeline.
Let's walk through the workflow step by step.
Jotform Trigger
The workflow begins with a Jotform Trigger.
Whenever a customer submits the feedback form, this trigger automatically activates the workflow. Inside the node, you simply connect your Jotform credentials and select the form that will be used.
Once configured, every new form submission will automatically start the workflow.
Set Parameters Node
The next step is a Set Parameters node.
This node defines reusable variables that are used later in the workflow. In this project, it stores the operational team email address. By defining this value once, the workflow becomes easier to maintain because it avoids repeating the same value in multiple nodes.
Create Customer Feedback in Airtable
The workflow then stores the raw feedback data in Airtable using the Create Customer Feedback node.
All form fields from Jotform are mapped directly into Airtable fields. This ensures that every submission is stored consistently inside the Customer Feedback table.
At this point, the raw customer feedback has already been safely stored in the database.
Generate AI Analysis (LLM Chain)
Next comes the most important step in the workflow: AI analysis.
The Generate AI Analysis LLM Chain node sends the feedback data to OpenAI. The prompt instructs the AI to analyze the feedback and return a structured response.
The input sent to the AI includes:
Service type
Rating
Feedback message
From these inputs, the AI generates several outputs, including:
AI sentiment
AI category
Urgency level
Feedback summary
Priority score
Confidence score
Root cause
Recovery direction
Recovery message
This workflow uses the GPT-4.1 mini model, although other models can also be used depending on your needs.
To ensure reliability, the workflow also uses a structured output parser. This guarantees that the AI always returns valid JSON, which is essential for automation workflows.
Store AI Analysis in Airtable
After the AI generates the analysis, the results are saved into the AI Analysis table using the Create AI Analysis node.
At this stage, the system has successfully stored both:
The original feedback
The AI-generated insights
This structured data allows the team to easily review and analyze customer feedback later.
Conditional Branching: Positive vs Negative Feedback
Once the AI analysis is completed, the workflow splits into two different branches depending on the result.
Positive Feedback Branch
If the feedback is positive and the rating is greater than three, the workflow executes the positive branch.
Again, three actions are performed.
First, an appreciation email is sent to the customer thanking them for the feedback.
Second, the operational team receives an email containing the feedback and AI analysis.
Third, the workflow posts a Slack message in the positive channel so the team can celebrate good customer feedback.
This branching logic ensures that the team reacts differently depending on the nature of the feedback.
///insert screenshot for positive feedback email result
///insert screenshot for positive slack message result
Negative Feedback Branch
This branch runs when:
The AI sentiment is negative, or
The rating is less than or equal to three
When this condition is met, three actions are triggered.
First, the system sends an email to the customer containing a recovery message.
Second, an email notification is sent to the operational team, including both the customer feedback and the AI-generated insights so the team can respond quickly.
Third, a Slack message is posted to the urgent channel to alert the team immediately.
///insert screenshot for negative feedback email result
///insert screenshot for urgent slack message result
Summary
In this project, we built a complete AI-powered customer feedback analysis system using n8n, Jotform, Airtable, and OpenAI.
The automation collects feedback from customers, analyzes it using AI, stores the results in a structured database, and automatically notifies both customers and internal teams.
The key benefits of this system include:
Automatically analyzing customer feedback with AI
Structuring insights such as sentiment, urgency, and root cause
Storing all feedback and analysis in Airtable
Automatically notifying teams through email and Slack
Helping teams prioritize and respond faster
Instead of manually reviewing every feedback message, teams can rely on AI to quickly highlight the most important insights and ensure that no critical feedback is missed.
If you want to build this automation yourself, you can download the full n8n workflow JSON from the video description and import it directly into your own n8n instance.
Thanks for reading, and I'll see you in the next project.
