AI-Powered Chatbots
Overview
Designed and deployed three AI-powered chatbots at Tawuniya Insurance, leveraging Large Language Models and Retrieval-Augmented Generation (RAG) to automate internal operations and improve employee experience.
The Challenge
Internal HR operations were heavily manual:
Employees waited hours or days for answers to common HR questionsHR team was overwhelmed with repetitive inquiriesNo self-service option for policy lookups, leave balances, or benefits infoKnowledge was scattered across multiple documents and systemsArchitecture
RAG Pipeline
We implemented a Retrieval-Augmented Generation architecture:
Document Ingestion — HR policies, employee handbooks, and FAQ documents were processed, chunked, and embeddedVector Store — Document embeddings stored for fast similarity searchQuery Processing — User questions are embedded and matched against the knowledge baseLLM Generation — Context-enriched prompts sent to the LLM for accurate, grounded responsesConversation Management
Multi-turn conversation support with context preservationIntent classification for routing to appropriate knowledge domainsFallback to human agents for complex queriesConversation analytics for continuous improvementSecurity & Privacy
All data processed within company infrastructureRole-based access control for sensitive HR informationAudit logging for compliance requirementsResults
85% reduction in HR query response time92% accuracy on common questions3 chatbots deployed across different departmentsPositive feedback from 500+ employeesLessons Learned
RAG significantly improves factual accuracy over pure LLM approachesChunking strategy matters — we tested overlap sizes from 50 to 200 tokensFeedback loops are essential for continuous improvementGuardrails prevent hallucinations on sensitive HR topics