Harnessing AI in an internal chatbot for streamlined operations
Technologies
- Azure
- OpenAI
The project’s business needs
The client needed a comprehensive discovery phase to identify their organisation’s most time-consuming operational tasks. The goal was to find an AI-powered solution to streamline processes, reduce time spent on repetitive tasks, and ultimately provide more value.
They wanted to develop a generative AI chatbot tailored to the needs of the customer service team. This chatbot would be key in facilitating quick and effective responses to customer queries by leveraging data and understanding requests. By automating the structuring of reactions, a chatbot will save time and ensure consistency and accuracy in customer interactions.
Our responsibility
We designed a chatbot with AI capabilities to solve the most time-consuming and error-prone operational problems. The system we created is based on Azure OpenAI Services and uses large language model integration (GPT-3.5). We chose this technology for its ability to deliver comprehensive and user-centric responses while consistently prioritising data security.
Effective intent recognition is the foundation of bot effectiveness. That is why we have adopted a robust framework that includes the Azure Bot Framework and Azure Language Studio to ensure accurate identification and resolution of user queries.
We have customised the large language model to meet the specific needs of our clients. Through targeted training, its primary function is to analyse and provide insight into the financial performance of contracts based on input data. This focus enhances our client’s ability to provide accurate and relevant responses to financial performance analysis.
Results of collaboration
This AI-powered solution is tailored to streamline customer service operations, leveraging Azure OpenAI Services and GPT-3.5 integration. The chatbot improves response efficiency, consistency and accuracy, addressing essential business needs and challenges in handling customer queries.
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