Canada
Puerto Rico
USA
Belgium
France
UK
India
Client Background
The client is an IT arm of a global car maker and next-gen IT solutions provider in the automotive industry.
Client Need
We identified a common challenge where employees spend significant time navigating multiple HR documents to find answers to even basic queries. This often leads to frustration due to access-level restrictions, scattered information across multiple files, and lack of summarized, precise answers.
Large volume of HR documents spread across SharePoint sites makes information retrieval time-consuming.
Queries whose answers span across multiple documents result in incomplete or confusing responses.
Solution
We implemented an AI-powered Retrieval-Augmented Generation (RAG) architecture to build an HR chatbot capable of:
Answering queries from 60+ HR documents, including handbooks, policies, and scanned PDFs.
Using document-type-specific preprocessing, including OCR for scanned files and tailored pipelines for PDFs, PPTX, DOCX, etc.
Providing responses within seconds, along with the source document link that redirects users to the exact page containing the answer.
Supporting contextual follow-up questions in a natural conversational interface.
Built using Python, FastAPI, LangChain for orchestration, and integrated with Azure OpenAI.
Realized Benefits
Significant reduction in time spent searching for HR-related information
Accurate, summarized, and contextual responses.
Always accessible virtual assistant requiring no prior technical knowledge.
Enhanced user experience and engagement through personalized interaction.
Platform-agnostic and scalable for other departments beyond HR.
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