Scalable AI Chatbot for a Manufacturer to Solve Customer Support Delays
Contents
We came together with a heating systems producer to introduce a flexible AI chatbot that supports customers in real time. The AI platform leverages LLMs with Retrieval-Augmented Generation (RAG), and offers a dynamic knowledge base and administration panel for easy management.
Challenge
The client aimed to reduce manual workloads, provide instant responses, and enhance engagement with both customers and internal teams. Solution? Introducing AI-driven customer support.
Client pains
The client’s customer support relied heavily on manual processes, some of the key challenges included:
- manual responses generating high operational costs and slow reaction times
- no 24/7 support for customers
- dispersed information across systems, making access to accurate answers difficult
- low personalization; standard systems could not adapt to user context
- limited insights into query handling and support effectiveness
They needed a solution that automated repetitive tasks, ensured personalized interactions, and integrated seamlessly with internal systems.
Project journey
Product design
The project began with designing a full-screen responsive interface for desktop and mobile. Wireframes and mockups defined the chat flow, including new conversations, continued chats, error states, and animations. User journeys were mapped to ensure intuitive navigation and a high-quality experience.
AI & backend development
The AI platform was built around an LLM with RAG architecture. The backend handled session and conversation management, retrieved data from CMS with agent's tool calls, multilingual queries, and API endpoints for communication with the frontend. A dynamic knowledge base was integrated via the existing CMS, enabling easy content updates.
Frontend development
A standalone chat application was developed with responsive layouts, real-time conversation updates, and interactive UI elements such as “AI is typing…” notifications and conversation history. The design prioritized mobile-first usability while maintaining consistency across devices.
DevOps & QA
CI/CD pipelines, environment setup, and automated tests ensured smooth deployment. Quality Assurance included functionality, security, performance, and user testing across multiple devices and browsers.
Launch & maintenance
The AI chatbot was deployed on a dedicated subdomain, improving scalability, branding, and SEO potential. Post-launch efforts focused on bug fixes, analytics monitoring, and optimization for performance and user experience.
Results
- Fully functional AI chatbot platform launched as a dedicated subpage
- Reduction in manual customer support workload
- Faster and more accurate responses, available 24/7
- Personalized interactions based on query context
- Unified access to accurate information for customers and staff
- Stronger branding and SEO visibility
- Scalable architecture ready for future features and integrations
