Ashu Dubey is the Co-founder and CEO of Alhena where he is building transformative customer experiences using Agentic AI. Before Alhena, he drove product growth at LinkedIn.
Most AI chatbots rely on vector databases alone. Alhena uses a knowledge graph to preserve structured product relationships, delivering hallucination-free answers that drive 3x conversion rates. Here's the technical reasoning behind that architectural choice.
The single biggest lever for retrieval quality isn't the embedding model or the retriever. It's what you retrieve against. Alhena's Contextualizer pipeline runs four parallel query rewriters before any vector search fires, transforming raw conversational messages into retrieval-ready queries.
Zendesk measures tickets resolved without a human. It can't measure tickets that were never created. The three rings of deflection framework reveals why pre-ticket prevention is the biggest cost lever in ecommerce support, and how to finally quantify it.
The full engineering walkthrough of Alhena's AI training pipeline. From a single URL to a searchable, answerable knowledge base: auto-discovery, semantic chunking, hybrid embeddings, five parallel vector databases, and atomic version swaps.
After every AI response, 3-5 tappable chips appear, each LLM-generated from conversation context and retrieved knowledge. Five design decisions make them convert: async generation, knowledge grounding, intelligent silence, diversity constraints, and a self-reinforcing discovery loop.
A technical deep dive into Alhena Voice's real-time architecture: dual WebSocket streaming, native audio understanding without STT in the critical path, triple barge-in cancellation, and the seven engineering decisions that compound to deliver sub-second conversational AI over the phone.