Reducing Customer Service Costs by 90% at Morele.net
AskSpot automated repetitive inquiries and enabled expert-level AI advisory for one of Poland’s largest e-commerce platforms.
01
Client
Morele.net
02
Industry
IT & consumer electronics e-commerce
03
Monthly visitors
5 million
04
Monthly orders
100,000
05
Customer service team (pre-AI)
50 agents
06
Experience
20 years in market
Key Results
90%
Reduction in customer service costs
50 → 5
Customer service headcount
90%
Customer satisfaction score
< 5 min
Average response time
Solution Used
AskSpot AI Chat
FAQ & Order Automation
Technical AI Advisory
API Integration with Internal Systems
“Ask the Expert” Self-Service Feature
The Challenge
Morele.net handles tens of thousands of inquiries monthly across a catalog of IT and electronics products.
Key issues included:
- Over 40% repetitive inquiries
- High operational costs
- Complex technical advisory needs
- Long response times
Implementation Goals
- Automate repetitive customer service inquiries
- Reduce operational costs
- Provide expert-level technical advisory via AI
- Improve response times
- Maintain high customer satisfaction
The Solution
Part 1:
Automation of Repetitive Inquiries
AskSpot automated frequently asked questions including:
- Order tracking
- Returns and policies
- Basic product information
The AI provided 24/7 support integrated with Morele’s internal systems.
Part 2:
Expert-Level AI Advisory
AskSpot delivered technical advisory for complex queries such as custom PC configuration. The system leveraged historical Q&A data and API integrations to provide real-time, accurate guidance.
A self-service “Ask the Expert” feature was also introduced on product pages.
Results
- Deep API integration
- Reinforcement learning and model refinement
- Community-driven Q&A enrichment
Why it worked
Deep API Integration
AskSpot connected seamlessly with internal systems, enabling real-time access to order and product data.
Continuous Learning
The AI improved over time using reinforcement learning and customer feedback refinement.
Expert-Level Knowledge Base
The system leveraged historical Q&A and technical documentation to deliver advanced advisory support.
Community-Driven Enrichment
User-generated Q&A contributions enhanced the AI model’s accuracy and depth.
