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An n8n-based WhatsApp chatbot that handles customer conversations and order automation from start to finish, delivered as part of a completed 80-hour engagement rated 5.0 out of 5. Client feedback: "Very strategic and great to work with! Knows his stuff and communicates clearly."
The agent uses Claude to understand customer intent, hold context across a conversation, and take the correct action, capturing orders, answering frequently asked questions, and updating records, while every interaction is logged for review.
Built with the same reliability standard used across all of my automation work: input validation, structured AI output instead of raw free text, and error handling that catches failures instead of letting messages disappear silently.
Stack: n8n, Claude AI, WhatsApp, webhooks and API integration.
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
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Experimented a bit today with Krea and image generation for a case study I’m putting together around AI EarPods connected to OpenAI.
The focus has been on creating fashion-forward product imagery and art directing a world that feels specific to the identity, rather than just generating nice-looking AI images.
The trickiest part has been product consistency. Especially getting the EarPods to actually sit snug in the ear. If you’ve worked through this process, you probably know the struggle 😅
Simply telling AI to “make it fit more snug or in the ear” doesn’t always work. It loves to reinterpret the product every time.
Still experimenting, but getting closer. If anyone has found a good workflow for keeping products consistent across AI-generated shoots, I’d love to hear it!
TOP!
Building AI chatbots that make customer support smarter and more efficient. 🤖
I develop custom AI chatbots that can:
• Answer customer questions instantly
• Understand natural language
• Work with business documents and knowledge bases
• Provide helpful, context-aware responses
• Support real business use cases
From simple LLM chatbots to RAG-powered assistants, I focus on building practical AI solutions that businesses can actually use.
#AIChatbot #ChatbotDevelopment #RAG #LLM #GenerativeAI #AI #Python
Most people don’t avoid automation because they lack ideas.
They avoid it because turning “when this happens, do that” into a working system can swallow an afternoon.
n8n has just introduced an assistant that can take a plain-English request, build the workflow, run it, and help fix what breaks.
That makes the first step easier. But there’s still one thing the tool can’t decide for you: what should happen when reality doesn’t follow the happy path.
Before automating a task, write down:
What starts it?
What should happen?
When should a person step in?
If you could automate one annoying task this week, what would it be?
