Intelligent Enterprise Policy Engine
I led the design and development of a production-ready intelligent conversational agent that summarizes, compares, and queries enterprise policies and compliance documents using DeepLake + Azure OpenAI. The system highlights updates and integrates multimodal understanding using LLMs and Vector databases to streamline internal knowledge retrieval.
The problem
Conventional enterprise document systems are limited in their ability to handle dynamic employee interactions and evolving policy documentation. Administration teams are burdened with high volumes of repetitive queries—such as internal procedures, policy clarifications, and operational steps—which require manual responses, leading to inefficiency and delay. Additionally, comparing old and new versions of compliance documents (like IT policies, benefits, and regulatory 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 operational 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 enterprise content management systems and seamless collaboration with administration 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 Enterprise Automation & Policy Intelligence System
This system addressed key limitations of conventional enterprise systems by integrating:
Real-Time Document Summarization: Automated the summarization of lengthy internal 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 internal procedures, policies, and compliance, with continuity across sessions.
Automated Policy Comparison Engine: Designed a document difference engine that compares historical and new versions of compliance documents to highlight semantic and structural changes (e.g., IT guidelines, vendor policies, code of conduct).
Semantic Search for Instant Answers: Enabled employees to query any company-related document or policy using natural language and get accurate answers instantly via a semantic search layer.
Reduced Manual Overhead: Cut down repetitive administrative tasks by up to 70%, freeing teams to focus on strategic functions and improving response time for employee concerns.
Improved Strategic Decision-Making: Provided the leadership with visibility into evolving policy documents and employee concerns via analytics dashboards powered by change detection and usage patterns.