How we reduced customer service queries by 50%, improved first-contact resolution by 35%, and built GDPR-compliant AI systems processing 2M+ interactions annually for Ireland's second-largest energy supplier.
SSE Airtricity, Ireland's second-largest energy supplier serving 750,000+ customers, engaged TechEvolveAI to transform their customer service operations through production AI systems. Over 18 months, we reduced customer service queries by 50%, improved first-contact resolution rates by 35%, and built GDPR-compliant AI systems that processed 2M+ customer interactions annually.
The engagement demonstrated how governance-first AI implementation enables rapid deployment in highly regulated industries—achieving full production deployment in just 6 months while maintaining 100% GDPR compliance.
SSE Airtricity operates in Ireland's competitive energy market, where customer service quality directly impacts retention rates. The company faced mounting pressure from rising customer expectations for instant service, increasing operational costs from high call volumes, and stringent regulatory requirements (GDPR, energy sector regulations, data protection standards).
500,000+ annual inquiries with 60% being repetitive questions about billing, meter readings, and tariff changes. Agents overwhelmed by routine queries.
Energy consumption patterns create predictable demand spikes (winter heating, summer cooling), overwhelming customer service during peak periods.
Must comply with GDPR, energy sector regulations, and consumer protection standards. AI systems required built-in compliance from day one.
Multiple legacy systems (CRM, billing, meter data) needed seamless integration. Solution had to work within existing workflows, not as standalone pilot.
We conducted comprehensive discovery workshops involving customer service managers, frontline agents, compliance officers, IT teams, and operations leadership. These sessions identified 30+ potential AI use cases across the customer service journey. Rather than attempting to automate everything at once, we applied a value-impact matrix to prioritize use cases.
Billing inquiries, meter reading submissions, tariff comparisons, account balance checks (high volume + low complexity)
Complaint routing, payment plan setup, service interruption notifications (medium volume + medium complexity)
Energy efficiency recommendations, predictive maintenance alerts, personalized tariff optimization (lower volume + high complexity)
Before writing a single line of code, we designed a governance framework that would enable rapid AI deployment while satisfying regulatory requirements:
We built production-ready AI systems (not prototypes) using a hybrid approach combining rule-based logic for high-certainty scenarios and machine learning for complex pattern recognition:
Trained on 500,000+ historical interactions, classified customer intent across 50+ categories, achieved 92% accuracy on test data, supported Irish English patterns.
Analyzed query complexity, customer history, and sentiment to determine routing: simple queries → automated, medium → AI-assisted agent, high complexity → immediate human escalation.
Connected to SSE's internal knowledge base (billing rules, tariff structures, regulatory requirements), enabled accurate, up-to-date responses without manual updates.
We deployed the AI system using a phased rollout strategy that minimized risk while gathering real-world performance data:
Traffic deployed, monitored daily, gathered agent feedback
Scaled after validation, introduced AI-assisted mode
Complete deployment, continuous learning pipeline
From discovery to full deployment in 6 months—demonstrating how governance-first AI implementation accelerates time-to-value in regulated industries.
Rather than treating compliance as a constraint, we designed governance into the AI system from day one. This approach enabled faster deployment because regulatory review was integrated into development rather than being a separate approval gate. GDPR requirements became product features (transparency, explainability, data minimization) rather than obstacles.
We prioritized use cases by value and complexity, delivering measurable impact within 6 months rather than attempting to automate everything at once. This incremental approach built stakeholder confidence, secured ongoing investment, and allowed us to learn from real-world performance before scaling.
We designed AI systems to augment human agents rather than replace them. Agents received AI-generated insights and suggestions, improving their effectiveness while maintaining human judgment for complex or sensitive situations. This collaborative approach improved both customer outcomes and agent satisfaction.