Built an AI-powered customer support platform designed to automate customer interactions, reduce support workload, and provide instant assistance using company documentation and knowledge base content.
The system leverages OpenAI, enterprise knowledge bases, and backend automation to deliver accurate, context-aware responses while seamlessly escalating complex issues to human agents when needed.
Key Features:
• OpenAI-powered conversational assistant
• Knowledge base search and retrieval
• FAQ automation system
• Context-aware customer responses
• Human escalation workflow
• Multi-channel support integration
• Customer interaction analytics
• Real-time response monitoring
• REST API architecture
• Enterprise-grade backend services
Business Impact:
✓ Reduced repetitive support workload
✓ Faster customer response times
✓ 24/7 automated customer assistance
✓ Improved customer satisfaction
✓ Consistent support experiences
✓ Lower operational costs
✓ Increased support team efficiency
Technology Stack:
• Java
• Spring Boot
• PostgreSQL
• OpenAI API
• REST APIs
• Docker
• Maven
• Knowledge Base Integration
Results:
• Automated approximately 80% of common support requests
• Reduced average response time from hours to seconds
• Reduced manual support workload by approximately 60%
• Improved scalability without increasing support staff
This solution demonstrates how AI-powered customer support systems can improve customer experience while significantly reducing operational overhead.
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!
Some quick mockups for Relay, an AI infrastructure platform concept built around model routing, observability, and performance.
Exploring what a mobile command center could look like for teams managing multiple AI models, with a focus on clear data, fast decisions, and a developer-first UX.
Product design, mobile UI, AI tools, and a little systems thinking all in one.
The question is basically: what's the context behind the decision?
AI can generate 100 packaging options. It can't tell you which one makes sense for a brand expanding into a market where soft pink reads very differently than it does back home. Or why the typography from three...