Our client, a global leader in safety, identification, and compliance solutions, aimed to improve its HR operations by implementing an advanced AI chatbot.
Our client, a global leader in safety, identification, and compliance solutions, sought to enhance its Human Resources (HR) operations through the implementation of an advanced AI chatbot. This chatbot aimed to streamline HR processes by providing employees and job seekers with accurate, immediate responses to their queries.
Our client wanted the ability to answer a high volume of HR inquiries, ranging from job searches and employment benefits to Environmental, Social, and Governance (ESG) queries in a fast, reliable, and easily consumable way. A chatbot could accomplish that and free up HR resources previously used to answer those questions.
Advanced Natural Language Processing (NLP): The chatbot is trained to understand and respond to a wide range of HR-related queries. Users can interact with it naturally.
Event-Driven Architecture: Automated the processing of newly uploaded documents, ensuring up-to-date information for the chatbot. This ensures the accuracy of the information used for the chatbot, while minimizing the costs of processing that information.
Retrieval-Augmented Generation (RAG): Document embeddings allow for accurate matching of user queries to relevant documents stored in the database via a similarity search. This means that the best and most relevant information is provided to Generative AI in order to return the best response to the user.
Using Google Cloud’s Application Default Credentials (ADC) we ensure all components of the system handle data securely. We implemented access controls to control management of the data set and access to the chatbot for testing.
The project team consisted of a project manager, software developers, and data scientists, working in an agile development environment.
PDF Document Parsing: Ensuring high-quality text extraction for some document formats proved to be challenging. Part of the dataset we used to build the chatbot included presentations. Unique formatting and tables with inconsistently merged columns work well for display purposes, but complicate text extraction. There are a large number of libraries in Python that help with this, each with its own strengths but none tackled every type of document perfectly.
Intent Detection: Detecting if a user was asking about available job listings vs job details required a fair amount of training in natural language understanding for the chatbot to correctly interpret the intent due to the nuances between questions about each topic.
A comprehensive testing strategy was implemented, including AI tests for relevance and hallucinations, integration tests, and user acceptance testing to ensure the chatbot met all functional and performance requirements.
The HR chatbot met all project goals. It proved to be accurate and fast in answering user questions. It solved previous issues with performance and hallucinations.
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