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"Developed an AI email automation system that evaluates incoming emails, routes them according to their intent, and initiates automatic responses or human review."
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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People don’t like to wait for your customer support system.
It shows that you don’t care enough for your users.
Introducing Assistia, a template for AI customer service automation platforms.
Designed specifically for SaaS companies.
It’ll reduce response times and cut support costs by up to 70%.
With its visual storytelling and social proof sections, strategic CTAs, and mobile-first design,
You can launch your website fast.
Check the comments for the links.
I’m currently building a RAG system for an e-commerce company, basically giving an AI assistant access to the company’s own knowledge and product information so it can give useful, context-aware responses instead of just generating random answers.
The interesting part isn’t just connecting an LLM.
It’s making sure the AI can:
→ Find the right information
→ Understand the user’s question
→ Retrieve relevant context
→ Give an accurate response
→ Actually be useful in a real business workflow
Still working on it, but I’m excited about where it’s going.
This is the kind of AI automation I enjoy building, systems that solve actual business problems, not just AI demos.
Building something similar? Let’s talk.
I designed and built an internal operating system for my agency that connects lead research, qualification, personalized drafting, content operations, project management, client approvals, reporting, and guarded AI assistance. It has three isolated experiences: an owner command center, a focused employee workspace, and a private client portal. The system uses role-based access, human-in-the-loop review, fact-checking gates, and automation safeguards. I led workflow discovery, product architecture, prompt and agent design, UX decisions, implementation, quality controls, and ongoing optimization.
