Improving customer service AI with 31% better resolution in 96 hours

The firm needed to develop a customer service AI for a major software company. The system had to understand technical queries accurately while maintaining the empathy and tone that foster customer trust and loyalty.

• Train AI on deep product knowledge for technical support
• Handle queries ranging from basic usage to complex integrations
• Adapt responses for different customer segments
• Keep pace with frequent product updates
• Balance automation efficiency with a human-like experience

Technical support presented unique hurdles: complexity, constant product changes, and high customer expectations. Previous chatbots had failed, frustrating customers with generic, unhelpful replies.

• Deep knowledge required across multiple platforms
• Queries spanning basic to advanced technical issues
• Varied communication styles needed by customer segments
• Frequent product updates complicating knowledge retention
• Generic chatbots damaging customer experience
• Efficiency needed without losing human touch

CleverX assembled a team of technical and customer success experts to create training content, refine AI responses, and ensure empathetic accuracy at scale.

Expert recruitment:

• Senior technical support engineers with decade-plus experience in enterprise software
• Customer success managers who understood relationship building through text
• Technical writers skilled in explaining complex concepts simply
• Product specialists familiar with common integration challenges

Training content creation:

• Thousands of real customer interactions annotated with ideal responses
• Decision trees for troubleshooting common technical issues
• Templates for maintaining empathy while delivering technical information
• Escalation triggers for issues requiring human intervention

Quality enhancement process:

• Multiple experts reviewing responses for technical accuracy and tone
• Consistency checks ensuring uniform quality across different query types
• Regular calibration sessions to align expert judgment
• Feedback incorporation from actual customer interactions

The training program unfolded in phases, combining large-scale annotation with expert oversight to ensure accuracy and fairness.

Weeks 1-2: Initial expert onboarding and familiarization with client's specific credit products and risk tolerance

Weeks 3-6: Experts annotated thousands of historical credit applications, providing detailed reasoning for approval or denial decisions

Weeks 7-10: Refinement phase where experts reviewed model outputs and corrected misunderstandings

Ongoing: Monthly expert consultations to address new credit products and emerging risk patterns

The supervised fine-tuning taught the model to detect subtle indicators of creditworthiness often overlooked by automated systems, such as improving financial trajectories and industry-specific income patterns.

Service excellence:

• More accurate first-response solutions to technical queries
• Better recognition of customer emotional states and appropriate responses
• Improved handling of multi-step troubleshooting processes
• Natural conversation flow that felt less robotic to customers

Operational efficiency:

• Reduced average handling time for routine technical issues
• Fewer escalations to senior support staff
• Consistent service quality across all hours and channels
• Better knowledge retention and sharing across the support organization

Customer experience:

• Higher satisfaction scores from faster, more accurate resolutions
• Reduced customer effort in explaining their problems
• More personalized interactions based on customer history
• Improved trust in automated support systems

Business impact:

• Increased customer retention through better support experiences
• Reduced support costs while maintaining quality
• Valuable insights from analyzed customer interactions
• Competitive advantage through superior technical support

This project was recognized by a prominent technology advisory firm for excellence in AI-driven customer experience transformation.