How Lime Credit Group Automated 60% of Customer Inquiries Without Expanding Its Support Team

As customer service operations grow, so does the pressure on support teams. Routine inquiries consume agents’ time, while manual processes make it harder to scale.

Lime Credit Group faced the same challenge: automate repetitive inquiries, bring text-based channels into a single environment, and free up agents to focus on complex customer issues — without increasing headcount.

Before implementing Flomni CS, a significant share of Lime Credit Group’s support operations was handled manually. Agents had to search the knowledge base for information, record customer data, and classify incoming inquiries themselves. This increased handling times and could lead to repeat contacts.

Non-standard cases were particularly time-consuming:

Another challenge was the classification of incoming inquiries. Inaccurate categorization made it difficult for the team to understand the true structure of customer demand and identify the issues that occurred most often. In some cases, customers could also fail to reach the right specialist.

Bringing text-based channels together was another priority. Customer inquiries came through web chat and E-mail, and the company needed a single workspace with complete interaction history and clear routing logic.

The project therefore went beyond simply reducing response times. Lime Credit Group wanted to build a more scalable support model with effective routing and more in-depth customer consultations.

By the time the team started working with Flomni CS, Lime Credit Group had already explored the market and compared several available solutions. The company was looking for a platform that would enable it to:

Data security was another critical requirement, including data localization in Russia, compliance with Russia’s Federal Law No. 152-FZ on Personal Data, and reliable data encryption:

Lime Credit Group ultimately selected Flomni CS as the technology foundation for the continued development of its support operations, from consolidating channels and automating routine inquiries to introducing AI-powered scenarios.

The next step was integrating Flomni CS into the support team’s day-to-day operations. The rollout was divided into several stages:

Over time, the solution evolved from a tool for handling incoming conversations into a multi-layered support environment where automation, AI, and human agents operate as part of the same workflow.

The first stage was to consolidate text-based communication within Flomni CS:

The Website widget was also configured to take the context of each inquiry into account:

In Dialogs, agents immediately receive the context they need to handle an inquiry. They can see the customer’s conversation history, key information from CRM fields, and tags that help identify the topic of the request.

The routing logic also takes the context of the inquiry into account, including:

Support is available around the clock, although most inquiries come in during daytime hours.

The next stage focused on automating routine inquiries. Before implementing Flomni CS, Lime Credit Group estimated that only around 10% of inquiries could be resolved without involving an agent.

To increase that share, the team configured a rule-based chatbot in Flomni CS using predefined scenarios. The first version covered the most common questions, including:

The team did not try to automate every possible situation from day one. It started with core scenarios built in Chatbot Builder and gradually expanded them with new topics, additional questions, and more detailed branches.

Today, the bot is involved in 100% of incoming conversations, while 60% of inquiries are resolved without an agent. At the same time, the team handles the current volume of text-based inquiries without expanding headcount.

Automation also changed the role of support agents. The capacity freed up from routine work could be redirected toward complex inquiries that require deeper consultation, individual analysis, and human expertise.

This created the foundation for the next stage: expanding automation with AI.

A scenario-based chatbot alone could not cover every inquiry. Customers do not always formulate their questions in the same way as predefined chatbot flows. The next challenge was therefore to enable the system to handle free-form inquiries by:

Flomni CS uses two AI tools for this:

At the same time, the automation remains controlled. If resolving an issue requires access to information about a specific loan, payment, or application, the conversation is handed over to an agent.

For example, a customer asks: “Can I repay my loan early?”

Customers do not need to determine the category of their question themselves, while agents join the conversation only when their expertise is actually needed.

For Lime Credit Group, it was important not to depend on a technical team for every change. The company wanted its own team to be able to develop and adjust automation independently:

After launch, a technical specialist from Lime Credit Group joined the project and gradually took over scenario management. Today, the specialist can:

This allows the team to make changes faster without submitting a separate request for every update. Flomni remains involved in complex integrations, new platform capabilities, and tasks that require support from the product or technical team.

Since implementing Flomni CS, Lime Credit Group has moved from predominantly manual inquiry handling to a multi-layered automation model:

The impact goes beyond reducing manual work. Automation has allowed the team to redistribute its capacity, giving agents more time for complex cases, in-depth consultations, and developing deeper expertise.

The project continues to evolve. The team is testing new AI capabilities, including automated quality assurance for customer interactions, expanding the range of topics covered by GPT-powered scenarios, and improving customer intent recognition.

Automation has allowed Lime Credit Group to redirect agent capacity toward complex cases, more in-depth consultations, and higher-value work. It has also changed how the team evaluates customer support, with greater attention paid not only to handling speed but also to consultation quality and customer loyalty.

The next stage is to continue developing the “AI Consultant” and integrate it with internal systems, allowing the company to gradually move more personalized scenarios to AI.

The role of human agents is also expected to evolve. As AI takes over more routine inquiries, employees will increasingly focus on complex and non-standard cases while monitoring the quality of AI systems themselves:

Flomni CS has become the foundation for the continued development of Lime Credit Group’s customer service, from consolidating communication channels and automating routine inquiries to scaling AI-powered support.