Introduction

Alhena - Generative AI for customer success

Alhena AI is a highly accurate generative AI system that learns everything about your company and products, automatically converses with your customers, and can take actions on their behalf.

Alhena AI

Key Features

Absolutely — here’s your Key Features section now, incorporating everything: the agentic architecture, your revenue/sales focus, and the proven strengths around accuracy, RAG, and low maintenance:

Key Features

  • Revenue-Driving AI Agents Alhena AI Agents do more than support—they sell. Like a personal assistant in a retail store, they engage customers from the moment they arrive, guide them through decision-making, and increase conversions, basket size, and retention. Think support meets sales, fully automated.

  • Action-Oriented Conversations Alhena AI Agents don’t stop at answering questions. They execute workflows—whether it’s recommending products, processing refunds, or tracking shipments—all within a single, natural conversation.

  • Best-in-Class RAG + Unmatched Accuracy & Relevance Alhena AI blends cutting-edge Agentic RAG with precise chunking and advanced retrieval pipelines to deliver deeply personalized, context-aware responses. It adapts to your company’s tone and content over time—no manual tuning needed. Every conversation feels human, relevant, and on-brand.

  • Agentic Architecture: Built for Scale & Custom Logic Powered by the OpenAI Agent SDK and a custom Agentic Framework, Alhena AI leverages:

    • Agentic RAG for adaptive context-aware retrieval

    • Agentic Chunking for intelligent content segmentation

    • MCP Server Model Context Protocol (MCP) for modular LLM-tool orchestration and real-time decisioning

  • Fast Setup, Low Maintenance Alhena AI is built to move fast. It can ingest messy, inconsistent knowledge from docs, spreadsheets, web pages, and more. Setup takes days—not weeks. And it’s low-lift to maintain, even as your business evolves.

Unique Attributes

  • Adaptive Learning from Real Interactions Alhena AI doesn’t just rely on pre-fed knowledge—it actively learns from ongoing customer conversations. It identifies patterns, friction points, and successful journeys, then continuously adapts its responses and actions to optimize for business outcomes.

  • Optimized for Checkout Completion By analyzing past user behaviors and conversions, Alhena AI Agents carve out the most effective paths to drive users toward checkout. Whether it’s resolving objections, surfacing the right products, or streamlining the buying process, the AI is always working to move the customer forward.

  • Intelligent Decision-Making at Runtime Every interaction is powered by real-time reasoning across tools, user history, and business logic. This means Alhena AI doesn’t just respond—it decides the best next step to help the user complete their journey.

🔧 Architecture Overview: How Alhena AI Works

Alhena AI is designed to power next-gen AI Agents that don't just chat — they act, reason, and drive outcomes. Our architecture is purpose-built for high accuracy, adaptability, and seamless integration into customer journeys.

This document outlines the major building blocks behind Alhena AI’s Agentic Framework and how we leverage the most advanced LLMs and retrieval pipelines in the industry.

🧠 LLM-Powered Intelligence

At the heart of Alhena AI is a multi-model strategy that uses the most advanced LLMs available today:

  • OpenAI GPT-4, 4.1, O3, O1, & GPT-4o – Multimodal capabilities, nuanced reasoning, and high contextual memory.

  • Anthropic Claude – Long context window (100K tokens) for rich, multi-turn interactions.

  • Cohere – Powerful reranking and semantic search optimization.

  • Google Gemini 2.5 Pro – Advanced reasoning across modalities like text, image, and audio.

  • Mistral Large 2 – Efficient for structured tasks, technical reasoning, and multilingual outputs.

  • Meta LLaMA 4 – Open-source and fine-tuned for specialized domain knowledge and cost-effective hosting.

These models are orchestrated based on the nature of the query, the customer's business domain, and the end goal of the interaction.

🧱 Modular Agentic Framework

Alhena AI is architected using modular and agentic principles. Here's how it works:

1. AgentConfig & Tool Definition

  • Each AI Agent is configured using an AgentConfig, which includes:

    • Tool definitions (via XML)

    • LLM routing rules

    • Skill-to-ability mappings

    • Brand voice integration

    • Guidelines for user/system handling

2. Custom Agent Behaviors

  • Our BaseAgent class enables modular reuse.

  • The AlhenaAgent extends it with:

    • Support for orchestrating tools and APIs

    • Custom model routing logic

    • Hand-off workflows (e.g. to human support)

    • App integrations using schema-based inputs

3. Real-Time Policy Enforcement

  • Every user query is passed through a policy enforcer to detect and block:

    • Out-of-context questions

    • Bot manipulation attempts

    • Hacking attempts

    • Escalation triggers for human handoff

🔍 Retrieval-Augmented Generation (RAG)

Alhena’s Agentic RAG pipeline transforms generic LLM outputs into precise, trustworthy answers.

End-to-End RAG Flow:

  1. User Query → Preprocessed into:

    • question_contextualization

    • original_query

    • hypothetical_answer (for forward rerank reasoning)

  2. Context Retrieval using FAISS or vector databases

  3. Reranking via Cohere or in-house logic

  4. Prompt Generation based on context + tools

  5. LLM Answer Generation

  6. Post-Processing & Link Generation

  7. Agent Workflow Execution if actions are required

🔄 Continuous Feedback Loop

  • User Feedback is captured after each conversation.

  • Feedback is used to:

    • Generate new FAQs

    • Update chunking and retrieval strategies

    • Improve rerank/answer generation quality

🧠 Agentic Chunking & Knowledge Graphs

For deep knowledge extraction and search precision:

  • Alhena runs Agentic Chunking to intelligently break down documents based on semantic structure, not just size.

  • Optimized chunks feed into a Knowledge Graph and product-level database.

  • This powers long-tail, high-intent search across complex product catalogs or policy documentation.

📈 Outcome-Driven AI Agents

The result? Alhena AI Agents are not just helpful — they’re revenue-generating. They:

  • Adapt to the user in real time

  • Execute tasks within conversations

  • Continuously learn and optimize

  • Shorten the path to conversion (e.g. checkout, booking, upgrade)

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