Luchi Insurance Reduced Appointment Booking Time by 32%: How an AI Agent Optimized Operator Work

At Luchi Insurance, the process of booking medical appointments for clients used to take up to 28 minutes per request. After implementing an AI agent, the median time was reduced to 19 minutes, and operators began handling five requests per hour instead of three. Concurrently, the cost of processing each request decreased from 120 to 100 rubles.

In medical insurance, every minute counts: delays in booking appointments mean not only a dissatisfied client but also potential health risks, and for the company, lost operator time and direct financial costs. Routine operations, such as summarizing dialogues and searching for necessary information, consume valuable hours that could be dedicated to more complex issues. However, this burden can now be lifted by intelligent systems.

The Reality of the Problem: Costly Appointment Booking Process

For an insurance company like Luchi, organizing medical appointments for clients is a complex and multi-stage process. Operators had to spend significant time summarizing previous dialogues, searching for necessary information in various databases, formalizing appointments, and providing clients with all the details. Each request demanded maximum concentration and attention, and any error could lead to a missed appointment or client dissatisfaction.

The median time for one appointment was 28 minutes, which significantly limited the operators' throughput. On average, one specialist could handle no more than three requests per hour. This led to increased personnel costs, service delays, and a decrease in the overall efficiency of the contact center.

The Path to an AI Agent: Why Existing Solutions Were Insufficient

Before implementing the AI agent, the company used standard CRM systems and knowledge bases. However, these required active operator involvement: manual information retrieval, data copying, and self-formulation of responses. These tools were more repositories of information than active assistants. They could not independently analyze the dialogue context, suggest ready-made solutions, or automatically summarize previous client interactions.

The company realized that a genuine breakthrough in efficiency required an intelligent tool that not only provided data but actively participated in the process, reducing the cognitive load on the operator and accelerating the completion of routine tasks. This led to the idea of implementing an AI agent.

How the AI Agent for Luchi Was Designed

The AI agent was developed as a comprehensive solution integrated into the operators' workflow. Its key tasks included:

• Dialogue Summarization. The agent automatically analyzed the history of communication with the client, highlighting key points and forming a concise summary. This allowed the operator to quickly grasp the context without having to reread long conversations.
• Operator Prompts. Based on the current dialogue and client history, the AI agent suggested the most relevant actions to the operator: response options, necessary clarifying questions, links to internal regulations, or forms to fill out.
• Quality Control. The system monitored compliance with protocols, the completeness of information provided, and the effectiveness of interaction, providing feedback for continuous improvement.

During design, it was considered that the AI agent should not replace humans but serve as a powerful assistant, taking on routine tasks and leaving decision-making and empathetic client communication to the operator.

Implementation and Team Adaptation

The implementation of the AI agent proceeded in stages. Initially, dialogue summarization and basic prompting functions were introduced. This allowed operators to quickly appreciate the benefits of the new tool without being overwhelmed by all features at once. Staff training was conducted through practical sessions where operators learned to interact with the agent, provide feedback, and utilize its capabilities to the fullest.

An important aspect was that the effect of the implementation was not solely attributed to generative AI. Simultaneously, work was done on classical machine learning, optimizing the digital client journey, and operational improvements. This comprehensive approach allowed for a synergistic effect and ensured smooth integration of the AI agent into the existing infrastructure.

Results of AI Agent Implementation

The 32% reduction in median booking time (from 28 to 19 minutes) significantly increased the contact center's throughput. Operators began handling 66% more requests (from 3 to 5 per hour), which directly impacted service accessibility and client satisfaction. The 16.7% reduction in processing cost per request (from 120 to 100 rubles) led to substantial savings for the company. These figures show that the AI agent not only simplified work but also brought tangible economic benefits.

How to Implement This in Your Business: Key Steps

The case of Luchi Insurance demonstrates that AI agents can radically transform operational processes. If your business faces similar challenges, here's where to start:

• Identify Routine Operations. Find processes where employees spend significant time on repetitive tasks: information retrieval, data summarization, forming standard responses. These are ideal candidates for AI agent automation.
• Start with "Quick Wins." Implement the AI agent in stages, beginning with functions that will bring quick and obvious benefits, so employees can see its effectiveness.
• Integrate into Existing Systems. The less employees have to change their habits and learn new interfaces, the faster the adaptation will occur.
• Focus on Comprehensive Impact. Evaluate not only the contribution of AI itself but also its synergy with other process and technology improvements.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager