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Claude is becoming our default choice for business AI agents.
Just finished an AI Sales Agent that:
- captures website leads
- qualifies them automatically
- updates the CRM
- tracks follow-ups
- generates conversion reports
The biggest win wasn’t AI conversation quality.
It was eliminating the “who owns this lead?” problem.
Built with Claude + automation workflows + CRM integration.
#claude
That’s a strong insight. Most teams don’t lose leads because of poor AI conversations; they lose them because ownership is unclear. Once qualification, routing, and follow-up are tied to CRM state, the AI agent becomes a revenue workflow rather than just a chatbot.
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Awesome!
Just shipped my own portfolio site — designed and built end to end.
No templates, no page builder. Figma for the design, then coded in HTML/CSS/JS + Next.js — working alongside AI tools to move faster without cutting corners. Custom animations, full mobile optimization, built in from day one.
Same process I bring to client work: design with intent, build it myself, ship something that actually performs.
love that custom cursor, so much cleverness in every detail!
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...
