The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Automated inquiry response and follow-up system built with n8n and OpenAI. Captures incoming inquiries, scores and prioritizes them with AI, and sends personalized follow-up messages automatically, logging everything to CRM or Google Sheets.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Trending
retrofuturism
Share your favorite designs from the past exploring the distant future. Or your own take on the aesthetic 🚀
halftone
Dots. Texture. Grain. Let's see your designs using the halftone technique.
Midjourney
Creatives are showing off what they're making with Midjourney.
colorgrade
Video editors sound off: How do you enhance the mood of your video in color grading?
Related posts
I recently built this workflow in n8n to automate the first stage of handling customer support requests.
The idea was to take an incoming ticket and let the system handle the repetitive work:
→ Receive the customer request through a webhook
→ Process and structure the incoming data
→ Use an AI Agent to understand the ticket
→ Classify it as Genuine, Spam, or Unclear
→ Route it based on the classification
→ Trigger the appropriate email action
→ Log the interaction in Google Sheets
I also added an Unclear path so that tickets the AI isn’t confident about don’t get blindly processed.
The goal isn’t to remove humans from customer support.
It’s to reduce the repetitive sorting and routing work, so the team can spend more time on the requests that actually need their attention.
Would you automate customer support differently? I’d love to hear how you’d approach it.
The Unclear lane is exactly the right guardrail. I’d add two production checks: redact PII before sending ticket text to the model, and measure false-positive/false-negative rates by category rather than a single accuracy score. Logging resolutions and human overrides back into...
I built an AI-powered n8n automation that generates personalized ice breakers for cold emails.
It takes a company’s website URL, analyzes the business, extracts key information, and uses AI to create a unique opening line for outreach.
Instead of manually researching every prospect, this workflow helps personalize cold emails at scale while saving hours of repetitive work.
Built with n8n + AI + web data extraction.
Strong use case. The quality control matters as much as the generation: I’d keep the source snippet and URL beside every ice breaker, then use a fallback when the evidence is thin instead of letting the model infer. Have you added a similarity check so repeat prospects don’t receive nearly identical openings?
I’ve officially launched StoryMint🚀
Turn one rough idea into a connected visual story - from planning the content to generating and editing the final carousel.
You can now support the launch on both platforms:
Peerlist: https://peerlist.io/vaibhavgareja/project/connected-story-ai-carousel-generator
Product Hunt: https://producthunt.com/posts/storymint-2/maker-invite?code=AO1IjN
And if you have a minute, try StoryMint → http://story-mint.com
Would genuinely love your feedback. What would you improve?
Looking too nice