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 escalations create blank tickets tagged 'customer needs help.' AI-structured tickets include conversation summaries, detected intent, sentiment scores, and recommended resolutions, so agents start at minute three of the problem, not minute zero.
Most AI dashboards show deflection rates. Alhena AI shows you a P&L. This post decodes every tab, metric, and formula in the analytics dashboard, from agent performance and trending topics to revenue attribution and CSAT breakdowns.
Macros are static, context-blind, and stale. AI replies built from your resolved ticket history generate contextual drafts shaped by how your best agents actually handled similar cases. Here is the mechanism behind it and the migration framework for replacing canned responses.
Your AI agent needs five backends but you don't want five custom integrations. Alhena's MCP support lets you register any MCP server in the dashboard, auto-discover its tools, assign them per agent with dual-scope permissions, and call them at runtime alongside native tools.
Bloomreach built its reputation on AI-powered site search and merchandising. Alhena AI was built for conversational selling. This guide compares both platforms on features, pricing, deployment, and real revenue results to help ecommerce brands pick the right approach.
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.