GPT CoPilot for Time Tracking App


Client’s platform is a widely used time tracking software that helps users manage their working hours across multiple projects. With an increasing number of monthly users and a strong presence in the time management market, the client aimed to enhance its app by integrating OpenAI’s large language model (LLM) through the GPT CoPilot project. The goal was to provide a more intuitive and user-friendly experience for time tracking and project management.


Client faced the challenge of improving user engagement and simplifying the time tracking process. They wanted to offer users a more natural language-based interface to interact with the app, replacing the traditional graphical user interface. However, integrating OpenAI’s LLM into the app posed several challenges, including integration-related challenges, data-oriented challenges, and prompt engineering challenges.


To address the integration-related challenges, client collaborated with a dedicated team of AI and web technology experts. The interdisciplinary team worked on seamlessly integrating the LLM into the time tracking app, ensuring a smooth user experience. For data-oriented challenges client’s team utilized various data operations to enrich the LLM context. By incorporating user data, the LLM was tailored to provide more personalized and relevant responses, enhancing the overall user experience. Additionally, a prompt versioning system was implemented to track and optimize prompts for better accuracy and suitability of responses.


The integration of GPT CoPilot into the rime tracking app yielded positive outcomes. Users experienced a more intuitive and user-friendly time tracking interface by interacting with the app using natural language. The simplified interface led to faster and more efficient time tracking across multiple projects, resulting in increased user satisfaction. Client observed a steady retention rate and a growing number of new monthly users, indicating the success of the GPT CoPilot project in enhancing the app’s appeal and functionality.

Smart Tip

When integrating LLMs into time tracking applications, focus on providing a natural language interface that simplifies the user experience. Tailor the LLM responses to the specific needs of time tracking and project management, leveraging user data to enhance personalization and accuracy.

Smart Fact

OpenAI’s LLM represents one of the most advanced engineering projects in human history. With its vast number of model parameters and training infrastructure, the LLM offers superior capabilities in handling user conversations. Prompt engineering plays a crucial role in shaping the LLM’s responses, with factors like word order, sentence structure, punctuation, and slang phrases influencing the output.

Technologies Used

Python, OpenAI Python library, LangChain Python library for LLM context enrichment, FastAPI Python framework for integration and user interaction.

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