Background
A client in the industrial machinery business was overwhelmed with basic support inquiries—issues like incorrect initial settings or cable misconfigurations.
Solving these problems was straightforward, but the sheer volume was draining the support staff’s time.
The client didn’t specifically request a chatbot.
They simply wanted to reduce the support load.
That’s where a structural approach came in: filtering and self-service before users ever reached an agent.
The Challenge
Support was fully dependent on direct inquiries—no filters or pre-checks in place.
Even if a manual or FAQ existed, many users would skip it and ask immediately.
To break this cycle, we introduced an AI chatbot to handle simpler issues first, funneling only the more complex cases to human support.
This “conversational layer” would ensure the team focused on inquiries that truly needed human expertise.
Implementation
- Minimalist Manual Site
We built a straightforward manual with almost no fluff—just critical information, easy to access and read. - Flowise-Powered Chatbot
Flowise is a no-code tool that connects AI models (LLMs) with structured data or APIs, allowing us to create a conversational flow without heavy custom code.
We leveraged it to guide users through setup and troubleshooting. - Admin Dashboard and Tracking
A simple admin panel lets staff review chatbot queries and gather user feedback.
A satisfaction tracking system measures how effectively the bot resolves issues. - Next.js + PostgreSQL + Docker on VPS
By hosting our Next.js app in Docker containers on a VPS, we kept costs low while ensuring enough performance.
We also used caching to speed up response times and maintain a smooth user experience.
Outcome
- Many repetitive inquiries never reached the support queue
- The support team could focus on higher-value tasks
- The chatbot’s knowledge base and responses improved over time, informed by feedback loops
- Overall user satisfaction increased—not just maintained—because of faster, more targeted help
By adding a conversational filter in front of traditional support, we structurally reduced the total inquiry load and improved efficiency.
Reusability
This structure can be applied both internally and externally.
As long as the foundation is well-built, continuous improvement (iteration) will refine the chatbot’s responses.
Because Flowise allows no-code configuration, new staff or non-technical teams can update the bot without diving into code.
It also reduces individual dependency—support knowledge isn’t just in one person’s head but within a shared AI system.
Final Thought
What if your support team could work less while user satisfaction stayed the same or got better?
It’s possible if you tackle the issue at the structural level.
Sometimes the real value isn’t about solving a ticket quickly—it’s preventing the ticket from ever existing.
A chatbot and a lean manual site can do just that, giving both users and support staff more breathing room and higher-quality interactions.
