P7 Office and WinWork Cut Response Time to 60 Seconds: How AI Agents Transform Customer Service

In 2025, AI agents ceased to be an experimental technology and became an integral part of companies' operational activities. The Russian office suite P7 Office, facing an audience growth to 10 million users, reduced the average support response time to less than 60 seconds. Similarly, the WinWork platform, which manages work with self-employed individuals, was able to maintain its support staff despite a twofold increase in inquiries, keeping CSI at 93%.

Business growth is always accompanied by an increased load on customer support. Hundreds, and then thousands, of typical questions take up time from qualified specialists, slow down reactions to complex cases, and ultimately reduce customer satisfaction. Attempts to solve the problem by hiring new employees lead to an exponential increase in costs. This is a dead end when there is technology that allows mass inquiries to be processed autonomously, while maintaining quality and speed.

The Pain of Growth: How Customer Service Became a Bottleneck

For P7 Office, a leading Russian developer of office software, rapid audience growth to over 10 million users was, on the one hand, a success, and on the other, a serious challenge for customer support. Traditional channels, designed for smaller volumes, began to "drown." Users waited longer for answers, operators were overwhelmed with repetitive requests, leading to burnout and a decline in service quality.

A similar situation arose at WinWork, a platform for automating work with self-employed individuals and individual entrepreneurs. The number of support inquiries grew from 5,000 to over 10,000 per month. At the same time, the company aimed to maintain 24/7 support and high-quality service without expanding its operator staff, which seemed almost impossible given such growth. Both companies faced a choice: either put up with declining quality and rising costs, or look for a fundamentally new solution.

From Chatbots to AI Agents: Why Previous Solutions Failed

Companies already had experience with traditional chatbots or FAQ systems, but they did not provide the necessary flexibility and autonomy. Standard bots work according to rigid scripts: if a user's question differed slightly from the built-in template, the bot could not give an adequate answer and transferred the request to an operator. This did not solve the problem of overload, but only filtered the simplest requests.

Therefore, P7 Office and WinWork turned to the concept of an AI agent — an autonomous system capable of reasoning and independent action to achieve a goal. Unlike simple bots, an AI agent can not only answer questions but also perform complex multi-step processes: gather information from different systems, make decisions based on context, initiate actions, and even learn from its mistakes.

How AI Agents for Customer Service Were Designed

For P7 Office, the AI agent was designed as the first line of support, capable of independently handling up to 85% of typical inquiries. It had to accurately answer questions about the office suite's functionality, provide instructions, and solve basic user problems. A key requirement was response speed — less than 60 seconds.

For WinWork, the AI agent was conceived as a "digital operator," integrated with the existing UseDesk ticketing system. Its task was to automate the processing of typical requests related to document management, payments, and platform operation questions, while providing 24/7 support without human intervention. The agent had to not only answer but also route complex requests, gather necessary information before transferring to an operator to minimize processing time.

In both cases, the key design principles were:

• Autonomy. The agent had to resolve tasks as independently as possible without constant human oversight.
• Contextuality. The ability to understand a user's request not just by keywords, but based on the overall context of the dialogue.
• Integration. Seamless operation with internal knowledge bases, CRM, and ticketing systems.
• Escalation. A clear mechanism for transferring complex or non-standard requests to live operators, with prior collection of all necessary information.

Implementation and Adaptation: From Pilot to Daily Tool

The implementation of AI agents proceeded in stages. P7 Office began by training the agent on its extensive product knowledge base, gradually expanding its competencies. An important step was integrating the agent into existing communication channels so that users did not feel a transition to a new system. Support operators were involved in the agent's training process to allow them to correct its responses and improve efficiency.

WinWork, by integrating the AI agent with UseDesk, initially focused on the most common and repetitive inquiries. This allowed for quick demonstration of the solution's value and collection of feedback from support staff. Gradually, the agent "learned" new scenarios, and its area of responsibility expanded. A crucial aspect was training operators to work with the agent: how to interact with it effectively, how to adjust its work, and how to use it to increase their own productivity, rather than viewing it as a replacement.

Results: Numbers That Speak for Themselves

The implementation of AI agents brought significant improvements in customer service to both companies:

For P7 Office, this meant users received instant and accurate help, directly impacting their loyalty. For WinWork, it meant the ability to scale business without a proportional increase in operational costs for support, while maintaining high service quality.

How to Implement This in Your Company: A Step Towards Autonomous Customer Service

The cases of P7 Office and WinWork demonstrate that AI agents are not just a trendy novelty, but an effective tool for solving real business problems in customer service. If your support department faces similar challenges, here's where to start:

• Identify routine and high-volume inquiries. Begin by analyzing the most common support requests that require standardized answers. These are ideal candidates for automation.
• Train the agent on your knowledge base. The more comprehensive and up-to-date the information you provide to the AI agent, the more accurate and helpful its responses will be.
• Integrate the agent into existing systems. To avoid a "zoo" of tools, embed the AI agent into your CRM, ticketing systems, and communication channels.
• Phased implementation and monitoring. Start with a pilot on a small volume or specific type of inquiry, gradually expanding the agent's functionality and monitoring its performance.
• Involve operators. Your employees are the best experts on customer inquiries. Use their knowledge to train and improve the agent's performance, turning it into a tool to enhance their own efficiency.

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