In this webinar, we present the Sorgenia case study — one of Italy's leading energy companies, which deployed the Crafter.ai platform to transform its business customer service through an advanced AI chatbot. The project stands as one of the most significant examples of how conversational AI can be applied in a complex, regulated sector like energy.
The Context: Customer Care Challenges in the Energy Sector
The energy sector presents specific characteristics that make customer care particularly challenging:
- High contact volumes related to bills, contracts, consumption, and administrative processes
- Marked seasonality with contact spikes following tariff changes or mass communications campaigns
- Continuously evolving regulations requiring always-accurate and current responses
- High customer expectations for immediate, precise answers
Sorgenia identified the AI chatbot as the solution to address these challenges without sacrificing service quality.
The Project: Objectives and Approach
The Sorgenia and Crafter.ai team defined clear, measurable objectives from the outset:
- Reduce call centre volume for routine enquiries
- Improve First Contact Resolution rate — resolving issues in a single interaction
- Provide 24/7 availability without increasing headcount
- Reduce average response times from hours to seconds
The methodology followed three main phases: analysis of the most frequent enquiry types, configuration of conversational flows, and integration with enterprise IT systems.
The Results: What Sorgenia Achieved
After deploying the Crafter.ai chatbot, Sorgenia recorded concrete, verifiable results:
- 75% of enquiries handled autonomously by the chatbot without human intervention
- 40% reduction in call centre volume for the main FAQ categories
- Average response time reduced to under 3 seconds compared to 4-8 minutes in telephone queue
- Customer Satisfaction Score (CSAT) improved thanks to always-on availability
The Key Capabilities of the Sorgenia Chatbot
The chatbot deployed by Sorgenia on Crafter.ai autonomously handles:
- Bill information: amounts, due dates, payment history
- Contract management: contract details, tariff type, contract changes
- Fault reporting: collecting reports and routing to technical teams
- Personal data updates: address changes, banking details for direct debit
- Products and offers: current tariffs, ongoing promotions
Integration with Enterprise IT Systems
One of the most technically significant aspects of the project was the integration with Sorgenia's enterprise IT systems. Crafter.ai connected with the CRM, billing system, and customer database to enable the chatbot to access real-time, customer-specific information.
This made personalised responses possible: "Your April bill is €98.50 and is due on 15 May" rather than generic information — significantly increasing perceived usefulness and customer satisfaction.
Lessons Learned: Best Practices for Customer Service AI
The webinar shares the best practices that emerged from the Sorgenia project, applicable to any company implementing a customer service chatbot:
- Start with FAQs: analyse the 20 most frequent enquiries and automate those first
- Design human fallback thoughtfully: define clear criteria for when the chatbot should hand off to a human agent
- Test with real users before go-live, collecting feedback to improve flows
- Monitor KPIs weekly and optimise flows based on real usage data
Scaling Customer Service AI: From Pilot to Enterprise Deployment
The webinar covers the journey from initial pilot to full enterprise deployment:
The pilot phase (weeks 1-6) focused on 5 high-frequency use cases, establishing the integration foundations and baseline metrics. After proving ROI in the pilot, the full deployment expanded to all customer service channels and 20+ use cases — supported by ongoing optimisation based on conversation analytics.
FAQ: Chatbot for Business Customer Service
How many training sessions does the chatbot need? With Crafter.ai, companies train the chatbot using existing documents (FAQs, manuals, policies) uploaded to the knowledge base. The system learns continuously from real interactions, improving over time without manual retraining.
Can the chatbot integrate with legacy systems? Yes. Crafter.ai provides standard REST APIs and webhooks for integration with any system, including legacy. Pre-configured connectors are available for the major CRM and ticketing systems.
How do we measure the ROI of a customer service chatbot? Key ROI indicators include: reduction in cost-per-contact, deflection rate (enquiries resolved without human involvement), CSAT improvement, and reduction in average handle time. Crafter.ai's analytics dashboard tracks all of these metrics in real time.