Introduction
Yueli (KGM Engine) is an enterprise-grade knowledge computation and AI application engine developed by HaxiTAG.
Designed for private enterprise data and complex business scenarios, it provides an integrated capability stack covering model inference, fine-tuning, Retrieval-Augmented Generation (RAG), and dynamic context construction. These capabilities are exposed through 48 production-ready, application-level APIs, directly supporting deployable, operable, and scalable AI application solutions.
At its core, Yueli is built on several key insights:
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In enterprise contexts, the critical factor for AI success is not whether a model is sufficiently general-purpose, but whether it can be constrained by knowledge, driven by business logic, and sustainably operated.
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Enterprise users increasingly expect direct, accurate answers, rather than time-consuming searches across websites, documentation, and internal systems.
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Truly scalable enterprise AI is not achieved through a single model capability, but through the systematic integration of multi-model collaboration, knowledge computation, and dynamic context management.
Yueli’s objective is not to create a generic chatbot, but to help enterprises build their own AI-powered Q&A systems, search-based question-answering solutions, and intelligent assistants, and to consolidate these capabilities into long-term, reusable business infrastructure.
What Problems Does Yueli (KGM Engine) Solve?
Centered on the core challenge of how enterprises can transform their proprietary knowledge and model capabilities into stable and trustworthy AI applications, Yueli (KGM Engine) addresses the following critical issues:
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Model capabilities fail to translate into business value: Direct calls to large model APIs are insufficient for adapting to enterprise knowledge systems that are complex, highly specialized, and continuously evolving.
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Unstable RAG performance: High retrieval noise and coarse context assembly often lead to inconsistent or erroneous answers.
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High complexity in multi-model collaboration: Inference, fine-tuning, and heterogeneous model architectures are difficult to orchestrate and govern in a unified manner.
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Lack of business-aware context and dialogue management: Systems struggle to dynamically construct context based on user intent, role, and interaction stage.
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Uncontrollable and unauditable AI outputs: Enterprises lack mechanisms for permissions, brand alignment, safety controls, and compliance governance.
Yueli (KGM Engine) is positioned as the “middleware engine” for enterprise AI applications, transforming raw model capabilities into manageable, reusable, and scalable product-level capabilities.
Overview of the Overall Solution Architecture
Yueli (KGM Engine) adopts a modular, platform-oriented architecture, composed of four tightly integrated layers:
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Multi-Model Capability Layer
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Supports multiple model architectures and capability combinations
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Covers model inference, parameter-efficient fine-tuning, and capability evaluation
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Dynamically selects optimal model strategies for different tasks
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Knowledge Computation and Enhanced Retrieval Layer (KGM + Advanced RAG)
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Structures, semantically enriches, and operationalizes enterprise private knowledge
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Enables multi-strategy retrieval, knowledge-aware ranking, and context reassembly
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Supports complex, technical, and cross-document queries
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Dynamic Context and Dialogue Governance Layer
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Constructs dynamic context based on user roles, intent, and interaction stages
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Enforces output boundaries, brand consistency, and safety controls
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Ensures full observability, analytics, and auditability of conversations
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Application and API Layer (48 Product-Level APIs)
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Covers Q&A, search-based Q&A, intelligent assistants, and business copilots
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Provides plug-and-play application capabilities for enterprises and partners
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Supports rapid integration with websites, customer service systems, workbenches, and business platforms
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Core Methods and Key Steps
Step 1: Unified Orchestration and Governance of Multi-Model Capabilities
Yueli (KGM Engine) is not bound to a single model. Instead, it implements a unified capability layer that enables:
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Abstraction and scheduling of multi-model inference capabilities
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Parameter-efficient fine-tuning (e.g., PEFT, LoRA) for task adaptation
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Model composition strategies tailored to specific business scenarios
This approach allows enterprises to make engineering-level trade-offs between cost, performance, and quality, rather than being constrained by any single model.
Step 2: Systematic Modeling and Computation of Enterprise Knowledge
The engine supports unified processing of multiple data sources—including website content, product documentation, case studies, internal knowledge bases, and customer service logs—leveraging KGM mechanisms to achieve:
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Semantic segmentation and context annotation
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Extraction of concepts, entities, and business relationships
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Semantic alignment at the brand, product, and solution levels
As a result, enterprise knowledge is transformed from static content into computable, composable knowledge assets.
Step 3: Advanced RAG and Dynamic Context Construction
During the retrieval augmentation phase, Yueli (KGM Engine) employs:
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Multi-layer retrieval with permission filtering
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Joint ranking based on knowledge confidence and business relevance
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Dynamic context construction tailored to question types and user stages
The core objective is clear: to ensure that models generate answers strictly within the correct knowledge boundaries.
Step 4: Product-Level API Output and Business Integration
All capabilities are ultimately delivered through 48 application-level APIs, supporting:
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AI-powered Q&A and search-based Q&A on enterprise websites
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Customer service systems and intelligent assistant workbenches
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Industry solutions integrated by ecosystem partners
Yueli (KGM Engine) has already been deployed at scale in HaxiTAG’s official website customer service, the Yueli Intelligent Assistant Workbench, and dozens of real-world enterprise projects. In large-scale deployments, it has supported datasets exceeding 50 billion records and more than 2PB of data, validating its robustness in production environments.
A Practical Guide for First-Time Adopters
For teams building an enterprise AI Q&A engine for the first time, the following path is recommended:
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Start with high-value, low-risk scenarios (website product Q&A as the first priority)
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Clearly define the “answerable scope” rather than pursuing full coverage from the outset
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Prioritize knowledge quality and structure before frequent model tuning
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Establish evaluation metrics such as hit rate, accuracy, and conversion rate
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Continuously optimize knowledge structures based on real user interactions
The key takeaway is straightforward: 80% of the success of an AI Q&A system depends on knowledge engineering, not on model size.
Yueli (KGM Engine) as an Enterprise AI Capability Foundation
Yueli provides a foundational layer of enterprise AI capabilities, whose effectiveness is influenced by several conditions:
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The quality and update mechanisms of enterprise source knowledge
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The maturity of data assets and underlying data infrastructure
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Clear definitions of business boundaries, permissions, and answer scopes
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Scenario-specific requirements for cost control and response latency
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The presence of continuous operation and evaluation mechanisms
Accordingly, Yueli is not a one-off tool, but an AI application engine that must evolve in tandem with enterprise business operations.
Conclusion
The essence of Yueli (KGM Engine) lies in helping enterprises upgrade “content” into “computable knowledge,” and transform “visitors” into users who are truly understood and effectively served.
It does not merely ask whether AI can be used for question answering. Instead, it addresses a deeper question:
How can enterprises, under conditions of control, trust, and operational sustainability, truly turn AI-powered Q&A into a core business capability?
This is precisely the fundamental value that Yueli (KGM Engine) delivers across product, technology, and business dimensions.
