Reducing Technical Support Bottlenecks with an AI Knowledge Assistant
1. The Challenge
Gemini Data Loggers has been designing and manufacturing environmental monitoring systems for over 40 years. Their products are known for being reliable, long-lasting, and widely used across multiple industries.
However, much of the product range has remained unchanged for decades. While this speaks to the quality of the hardware, it also means that a large portion of the supporting technical information is dated, fragmented, and difficult to navigate.
Internally, the biggest challenge was a lack of redundancy in technical support. The business relied heavily on a single, highly knowledgeable support specialist. When unavailable due to site work, leave, or illness, other team members struggled to confidently handle technical queries.
Key Issues Included:
AI Efficiency Review
2. The Approach
We started by analysing how technical queries were handled day-to-day, identifying where delays and friction occurred.
It quickly became clear that the issue wasn’t a lack of information, but a lack of accessible, structured knowledge.
The goal was to:
- Centralise existing product information
- Make it easy to search and retrieve accurate answers
- Enable non-technical staff to confidently respond to queries
- Reduce dependency on a single support contact
Delivering AI-Powered Solutions
3. The Solution
We developed an internal AI-powered knowledge assistant designed to provide fast, accurate answers to technical questions.
This system brought together:
- Product Manuals & Data Sheets
- Website content
- Existing documentation and resources
All information was organised into a single, searchable knowledge base.
The assistant was designed to:
- Pull answers directly from trusted internal sources
- Provide clear, concise responses to specific questions
- Guide staff through common technical issues without requiring deep product knowledge
The system is now being expanded to include historical support cases and email conversations, further strengthening its ability to handle real-world queries.
Following successful internal use, the same system is planned to be deployed as a customer-facing AI chatbot on the company website.
4. The Outcome
The impact was immediate and practical.
- Faster response times to customer queries
- Reduced reliance on a single support specialist
- Increased confidence across the team when handling technical questions
- More consistent answers, regardless of who responds
- Improved customer experience with quicker, more direct solutions
What was once a bottleneck is now a shared capability across the business.
Instead of passing customers between departments or waiting for availability, multiple team members can now step in and provide accurate support when it’s needed.