AI Knowledge Assistant and RAG Product

Overview

Veltrix innovation built an AI-powered knowledge assistant using retrieval-augmented generation (RAG) to help users navigate complex product information, receive context-aware answers, and complete guided onboarding workflows efficiently.

Challenge

Users faced significant friction during onboarding due to dense documentation and a lack of real-time, context-specific guidance. The business needed a scalable AI solution that could replace static FAQs and reduce manual support load for common user queries.

Architecture & Scope

The product was built as a conversational AI layer on top of structured product knowledge:

• RAG Pipeline: Document ingestion, chunking, embedding, and vector storage for retrieval
• Query Processing: User queries matched to relevant knowledge chunks using vector similarity search
• Response Generation: Context-enriched prompts sent to the LLM for accurate, grounded answers
• Onboarding Flows: Guided multi-step journeys powered by AI-driven decision logic
• Backend APIs: Node.js services connecting the frontend to the AI pipeline

Technical Approach

The AI layer was built using vector search for retrieval, with a React frontend for the user-facing interface. The backend handled document embeddings, vector similarity queries, and prompt assembly before LLM inference. Responses were grounded in product-specific content to minimize hallucination and maintain accuracy at scale.

Impact

The assistant significantly reduced friction in the user onboarding journey, providing instant contextual answers without requiring support team intervention. The RAG approach ensured responses were consistently accurate, grounded in real product data, and adapted to the specific context of each user query.