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.
The existing Unified Memory post explains what memory does. This one explains how it works: two-layer storage, 5W1H fact extraction, evolution chains for version control, and privacy isolation at the database level.
Most AI copilots produce drafts in a single company voice. Alhena's Agent Assist observes how each agent edits AI-drafted replies and calibrates future drafts to match their individual communication style, compounding accuracy over time without explicit training.
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.
AI is absorbing category pages, FAQ pages, and comparison pages into conversational interfaces. This framework scores every ecommerce page on two axes: does a human need to see it, and does AI need to read it?