Customer Feedback Processing
Receives customer feedback via webhook, analyzes sentiment with AI, generates a branded PDF report, emails it to the user.
What it does
You automatically receive AI-analyzed sentiment reports on customer feedback directly in your inbox. This workflow extracts key insights from incoming submissions, generates branded PDF summaries, logs the data for tracking, and alerts your team via Slack.
Step by step
- Configure the webhook endpoint to accept POST requests with a JSON payload containing customer feedback details.
- Set up the API call structure for OpenAI using the chat completions endpoint with a system prompt defining sentiment analysis and insight extraction rules.
- Define the HTML template structure that maps extracted insights into a readable format before converting it to PDF.
- Configure the Google Sheets integration to append rows with timestamp, customer email, sentiment score, and key insights.
Where the LLM does the work
- Craft the system prompt for OpenAI to ensure consistent extraction of actionable insights from varied natural language feedback.
- Generate dynamic HTML content that adapts the tone of the personalized email response based on the detected sentiment (positive vs negative).
- Refine the PDF styling and layout logic to ensure branded elements like logos and headers render correctly across different devices.
Watch out for
OpenAI API costs can escalate quickly with high volume, so implement caching for identical feedback inputs.
Email delivery may fail if Gmail authentication tokens expire, so add a refresh token mechanism to the workflow.
PDF generation libraries often struggle with complex CSS, so keep the HTML template simple and test rendering early.
Tools that fit
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Customer Feedback Processing: Receives customer feedback via webhook, analyzes sentiment with AI, generates a branded PDF report, emails it to the user. ## Specification - What it does: You automatically receive AI-analyzed sentiment reports on customer feedback directly in your inbox. This workflow extracts key insights from incoming submissions, generates branded PDF summaries, logs the data for tracking, and alerts your team via Slack. - Trigger: Triggered by an event (Event · on webhook submission) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - OpenAI: Sentiment analysis and insight extraction - Gmail: Sending personalized thank-you emails with PDF attachments - Google Sheets: Logging feedback data for analytics and audit trails - Slack: Notifying team channels with real-time summaries ## Known pitfalls, handle each one explicitly in your implementation 1. OpenAI API costs can escalate quickly with high volume, so implement caching for identical feedback inputs. 2. Email delivery may fail if Gmail authentication tokens expire, so add a refresh token mechanism to the workflow. 3. PDF generation libraries often struggle with complex CSS, so keep the HTML template simple and test rendering early. ## Reference implementation https://n8n.io/workflows/9489 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automate-customer-feedback-processing/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for support teams drowning in inbound and looking to triage faster, not just harder.
Also fits E-commerce Operator.