Retrieval-Augmented Factuality

This case study highlights how Tesvan implemented retrieval-augmented factuality checks using context-sensitivity techniques to power a modern AI chatbot. The solution leverages large language models (LLMs) and embedding-based retrieval to provide contextually relevant, human-like, and factually accurate responses.

By ingesting website content and internal documentation, the chatbot processes domain-specific data into embeddings stored in a vector database. Through a Retrieval-Augmented Generation (RAG) approach, the system grounds answers in verified information, minimizing hallucinations and improving trustworthiness.

A state machine manages conversation flow, intent classification, and tool integrations (CRM, scheduling, and follow-up). Built on a scalable API framework, the chatbot delivers seamless interactions across both CLI and web interfaces with rapid response times.

• Preventing LLM hallucinations and fabricated outputs.
• Preserving contextual consistency in complex conversations.
• Managing integration with multiple enterprise tools.
• Achieving fast responses while retrieving from vector databases.

• Built a RAG pipeline to connect chatbot answers with source-of-truth data.
• Applied retrieval-augmented factuality checks using context-sensitivity techniques at every response step.
• Deployed a state machine for coherent, context-sensitive dialogue.
• Integrated CRM and scheduling systems for automation.
• Delivered on a scalable API framework supporting both CLI and web use.

By implementing retrieval-augmented factuality checks using context-sensitivity techniques, Tesvan achieved significant performance and business gains: