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AI-Powered Knowledge Management Chatbot for Veolia Energia Polska

websensa.com

Utilities

Manufacturing

Environmental Services

Challenges in Streamlining Information Access

Employees faced difficulties accessing technical documentation and HR processes due to fragmented, non-searchable company documents. This led to operational inefficiencies, increased time spent on information retrieval, and higher costs associated with knowledge management.

About the Client

A leader in resource management, environmental protection, and public services

Project Goals for AI-Powered Knowledge Chat

Create a conversational chatbot for seamless access to technical and HR documents
Enhance operational efficiency through rapid information retrieval
Reduce costs associated with document management and employee training
Improve decision-making accuracy via reliable, source-cited responses
Scale knowledge accessibility across departments beyond initial technical implementation

Core System Functionalities and Features

Natural language processing (NLP) chat interface
Document search with source citation
Technical troubleshooting and safety protocol guidance
HR process automation (benefits, insurance, delegation)
Context-based search with permission controls
Real-time updates from multiple data sources

Technologies for Implementation

Large Language Models (LLMs)

Retrieval-Augmented Generation (RAG)

Python (Django, Langchain)

Vue.js frontend framework

PostgreSQL relational database

Qdrant vector search engine

Google Cloud Platform (GCP)

System Integrations

Internal document repositories
HR management systems
User authentication infrastructure
Procurement and compliance databases

Non-Functional Requirements

Prevent AI hallucinations through data grounding
Role-based access control (RBAC)
High-availability cloud architecture
Jailbreak prevention mechanisms
Scalable vector search capabilities
Compliance with data security regulations

Expected Business Impact of Knowledge Chat Implementation

The solution is projected to reduce information retrieval time by 70%, decrease knowledge management costs through automated document processing, enable data-driven decision-making via accurate information access, and support organizational scalability by handling 2500+ monthly queries across 400+ users with potential for wider deployment.