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.
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.