AI-Powered HRM System
I led the design and development of an production-ready intelligent chatbot system that summarizes, compares, and queries HR policies and documents using DeepLake + Azure OpenAI and that intelligently, highlights updates, and integrates multimodal understanding using LLMs and Vector databases.
The problem
Conventional Human Resource Management Systems (HRMS) are limited in their ability to handle dynamic employee interactions and evolving policy documentation. HR teams are burdened with high volumes of repetitive queries—such as leave balances, policy clarifications, and onboarding steps—which require manual responses, leading to inefficiency and delay. Additionally, comparing old and new versions of HR documents (like leave policies, benefits, and compliance rules) is error-prone and time-consuming. These systems also lack intelligent understanding of context or memory across employee sessions, resulting in fragmented user experiences. The absence of real-time document summarization, semantic search, and automated change detection significantly hampers decision-making and reduces overall HR responsiveness.
Tech stack & system design
The system uses Python, FastAPI, DeepLake (for embedding-based document retrieval), and Azure OpenAI’s GPT models. We built a summary-comparison-query pipeline with support for semantic search and document comparison. The system is designed to be flexible and scalable, allowing for easy integration with various HRMS systems and seamless collaboration with HR teams.
User Experience
Employees can upload documents, search by policy name, or ask questions in natural language. Each answer includes visual links to the referenced text with document and page number highlights. The system also provides a side-by-side comparison of old and new policy versions, highlighting changes. The chatbot remembers user interactions, allowing for context-aware responses and follow-up questions.
Project Outcome: Intelligent HRMS Automation & Policy Intelligence System
This system addressed key limitations of conventional HRMS by integrating:
Real-Time Document Summarization: Automated the summarization of lengthy HR policies and guidelines to deliver concise, contextual insights to employees.
Context-Aware Chatbot Interface: Built a memory-enabled conversational agent capable of handling employee queries regarding onboarding, leaves, policies, and compliance, with continuity across sessions.
Automated Policy Comparison Engine: Designed a document difference engine that compares historical and new versions of HR documents to highlight semantic and structural changes (e.g., benefits, leave policies, code of conduct).
Semantic Search for Instant Answers: Enabled employees to query any HR-related document or policy using natural language and get accurate answers instantly via a semantic search layer.
Reduced Manual Overhead: Cut down repetitive HR tasks by up to 70%, freeing HR teams to focus on strategic functions and improving response time for employee concerns.
Improved HR Decision-Making: Provided HR leadership with visibility into evolving policy documents and employee concerns via analytics dashboards powered by change detection and usage patterns.