Case Study: Jetbrains

Overview

JetBrains is a software development company which specializes in licensed software, including Integrated Development Environments (IDEs) for a variety of developers, ranging from students to industry professionals at Fortune 500 companies. As of 2025, over 11 million developers are using JetBrains products, and the company is growing at a rate of thousands of users per day. As JetBrain usage grows, their dedicated support team must address an ever-increasing volume of support requests, ranging from repetitive questions about licensing to technical questions about new advanced AI features.

Problem

JetBrains sought AI solutions as a way to support their customer success teams as their company grows.  The support needs of JetBrains fluctuate significantly, meaning preventing overstaffing or understaffing is a challenge.  Some of these spikes are seasonal, such as student questions about licenses around the start of the school year, or related to events such as new product releases. As a result, with a support team unaided by AI, maintaining low response times and high customer satisfaction rates year round would be difficult and expensive.

JetBrains maintains several information sources about how to address these issues, ranging from public facing articles, support websites, to internal guidelines accessible only to agents. However, the number of documentation sources leaves agents stitching together workarounds and add stress and resolution times.

Solution

QueryPal is built by an experienced machine learning team, which is constantly striving to implement the latest AI updates. The team is committed to using the latest Large Language Models to provide custom draft answers to JetBrains support agents, which the agents then can  provide to customers. When a ticket is created, QueryPal immediately begins drafting a response, meaning that often times a draft answer which includes citations for its reasoning is available by the time an agent opens a new ticket.

QueryPal can specialize in responses for a few distinct query types, ranging from account management questions to tech support questions regarding how to use the JetBrains products. As a result, QueryPal worked with the JetBrains team to provide custom solutions which utilize different sources and quality checks. Additionally, QueryPal has worked with JetBrains support teams in order to identify knowledge gaps or inconsistencies in support documents. In this way, QueryPal can help improve not only response times and customer satisfaction, but help technical documentation team members.

Conclusion/Impact

While JetBrains has expanded the categories for which QueryPal drafts answers, it has a close relationship with the QueryPal team and provides constant feedback, which in turn, improves the QueryPal solution. JetBrains provides metrics from agents directly, enabling the team to improve  answers and understand the impact of auto answers on an agent’s workflow; historically, over 92% of thousands of questions have been upvoted by agents.

Finally, QueryPal specializes in white glove service for its customers, JetBrains included, and has been able to provide custom AI powered categorization and analytics of the subject matter of queries. QueryPal’s partnership with JetBrains exemplifies how QueryPal, an expert in machine learning, can insulate a support team from the complex details which can take AI from helpful to distracting.

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