When a voice caller needs a human at 10 PM on a Sunday, most systems hit a dead end. Alhena Voice AI creates structured callback tickets with full transcripts, phone numbers, and urgency tags so Monday morning agents resolve issues in minutes.
Most chatbots follow a simple loop: receive message, generate response. Alhena does something different. A dedicated planner agent decides which specialists should handle each part of the request, then a replanner adapts as things unfold.
Smart speaker shopping failed. Phone-based voice AI is different. This post breaks down three production UX patterns: how voice add-to-cart uses consent-gated tool contracts, how handoffs preserve context across telephony transfers, and how checkout splits voice from payment.
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.