What is Aura AI?
Aura AI is a production conversational AI assistant designed for corporate and e-commerce customer support. Built on a Retrieval-Augmented Generation (RAG) architecture, it dynamically indexes customer websites and knowledge bases, allowing it to answer queries accurately without hallucinations or expensive model fine-tuning.
What Problem Does It Solve?
Traditional rule-based chatbots frustrate users with rigid script menus, while generic large language models frequently hallucinate facts or leak outdated company policies. Aura AI solves this by grounding every answer directly in company documentation with precise citations and conversational guardrails.
How Does It Work?
- Dynamic Web Ingestion: Ingests live company website URLs, sitemaps, and help articles, parsing clean textual hierarchy while filtering boilerplates.
- Vector Chunking & Embeddings: Text is intelligently chunked and converted into semantic vector embeddings stored in a high-speed vector index.
- Hybrid Semantic Search: When a user asks a question, Aura AI performs cosine-similarity matching to retrieve the most relevant knowledge passages.
- Strict Grounded Response Generation: The LLM synthesizes natural responses constrained strictly to the retrieved context, eliminating hallucinations.
Technology Stack
Python Retrieval-Augmented Generation (RAG) LangChain Vector Database (ChromaDB) OpenAI & LLM APIs FastAPI Web Scraping
Ahmed Raza's Contribution
Ahmed Raza served as the Lead AI Developer for Aura AI at INARA Technologies, designing the automated ingestion scraper, vector chunking strategies, and low-latency API integration powering the customer-facing interface.