HaxiTAG: data security

This white paper provides a systematic analysis and practical guide on how HaxiTAG Studio’s intelligent application middle platform activates unstructured data to drive AI value. It elaborates on core insights, problem-solving approaches, technical methodology, application pathways, and best practices.

Core Perspective Overview

Core Thesis:
Unstructured data is a strategic asset for enterprise AI transformation. Through the construction of an intelligent application middle platform, HaxiTAG Studio integrates AI Agents, predictive analytics, and generative AI to establish a closed-loop business system where “data becomes customer experience,” thereby enhancing engagement, operational efficiency, and data asset monetization.

Challenges Addressed & Application Value

Key Problems Tackled:

Unstructured data constitutes 80–90% of enterprise data, yet remains underutilized.
Lack of unified contextual and semantic understanding results in weak AI responsiveness and poor customer insight.
AI Agents lack dynamic perception of user tasks and intents.

Core Values Delivered:

Establishment of data-driven intelligent decision-making systems
Enhanced AI Agent responsiveness and context retention
Empowered personalized customer experiences in real time

Technical Architecture (Data Pipeline + AI Adapter)

Three-Layer Architecture:

(1) Data Activation Layer: Data Cloud

Unified Customer Profile Construction:
Integrates structured and unstructured data to manage user behavior and preferences comprehensively.
Zero-Copy Architecture:
Enables real-time cross-system data access without replication, ensuring timeliness and compliance.
Native Connectors:
Seamless integration with CRM, ERP, and customer service systems ensures end-to-end data connectivity.

(2) AI Intelligence Layer: Inference & Generation Engine

Predictive AI:
Use cases such as churn prediction and opportunity evaluation
Generative AI:
Automated content and marketing copy generation
Agentic AI:
Task-oriented agents with planning, memory, and tool invocation capabilities
Responsible AI Mechanism:
Emphasizes explainability, fairness, safety, and model bias control (e.g., sensitive corpus filtering)

(3) Activation Layer: Scenario-Specific Deployment

Applicable to intelligent customer service, lead generation, personalized recommendation, knowledge management, employee training, and intelligent Q&A systems.

Five Strategies for Activating Unstructured Data

Strategy No.	Description	Use Case / Scenario Example
1	Train AI agents on customer service logs	FedEx: Auto-identifies FAQs and customer sentiment
2	Extract sales signals from voice/meeting content	Engine: Opportunity and customer demand mining
3	Analyze social media text for sentiment and intent	Saks Fifth Avenue: Brand insight
4	Convert documents/knowledge bases into semantically searchable content	Kawasaki: Improves employee query efficiency
5	Integrate open web data for trend and customer insight	Indeed: Extracts industry trends from forums and reviews

AI Agents & Unstructured Data: A Synergistic Mechanism

Semantic understanding relies on unstructured data:
e.g., emotion detection, intent recognition, contextual continuity
Nested Agent Collaboration Architecture:
Supports complex workflows via task decomposition and tool invocation, fed by dynamic unstructured data inputs
Bot Factory Mechanism:
Rapid generation of purpose-specific agents via templates and intent configurations, completing the information–understanding–action loop

Starter Implementation Guide (Five Steps)

Data Mapping:
Identify primary sources of unstructured data (e.g., customer service, meetings, documents)
Data Ingestion:
Connect to HaxiTAG Studio Data Cloud via connectors
Semantic Modeling:
Use large model capabilities (e.g., embeddings, emotion recognition) to build a semantic tagging system
Scenario Construction:
Prioritize deployment of agents in customer service, knowledge Q&A, and marketing recommendation
Monitoring & Iteration:
Utilize visual dashboards to continuously optimize agent performance and user experience

Constraints & Considerations

Dimension	Limitations & Challenges
Data Security	Unstructured data may contain sensitive content; requires anonymization and permission governance
AI Model Capability	LLMs vary in understanding domain-specific or long-tail knowledge; needs fine-tuning or supplemental knowledge bases
System Integration	Integration with legacy CRM/ERP systems may be complex; requires standard APIs/connectors and transformation support
Agent Controllability	Multi-agent coordination demands rigorous control over task routing, context continuity, and result consistency

Conclusion & Deployment Recommendations

Summary:HaxiTAG Studio has built an enterprise intelligence framework grounded in the principle of “data drives AI, AI drives action.” By systematically activating unstructured data assets, it enhances AI Agents’ capabilities in semantic understanding and task execution. Through its layered architecture and five activation strategies, the platform offers a replicable, scalable, and compliant pathway for deploying intelligent business systems.

In the course of modern enterprises' digital transformation, adopting large language models (LLMs) as the infrastructure for natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) applications has become a prevailing trend. However, choosing the right LLM model to meet enterprise needs, especially testing and optimizing these models in real-world applications, has become a critical issue that every decision-maker must carefully consider. This article delves into several key aspects that enterprises need to focus on when selecting LLM models, helping readers understand the significance and key challenges in practical applications.

NLP Model Training Based on Enterprise Data and Data Security

When choosing an LLM, enterprises must first consider whether the model can be effectively generated and trained based on their own data. This not only relates to the model's customization capability but also directly impacts the enterprise's performance in specific application scenarios. For instance, whether an enterprise's proprietary data can successfully integrate with the model training data to generate more targeted semantic understanding models is crucial for the effectiveness and efficiency of business process automation.

Meanwhile, data security and privacy cannot be overlooked in this process. Enterprises often handle sensitive information, so during the model training and fine-tuning process, it is essential to ensure that this data is never leaked or misused under any circumstances. This requires the chosen LLM model to excel in data encryption, access control, and data management, thereby ensuring compliance with data protection regulations while meeting business needs.

Comprehensive Evaluation of Model Inference Quality and Performance

Enterprises impose stringent requirements on the inference quality and performance of LLM models, which directly determines the model's effectiveness in real-world applications. Enterprises typically establish a comprehensive testing framework that simulates interactions between hundreds of thousands of end-users and their systems to conduct extensive stress tests on the model's inference quality and scalability. In this process, low-latency and high-response models are particularly critical, as they directly impact the quality of the user experience.

In terms of inference quality, enterprises often employ the GSB (Good, Same, Bad) quality assessment method to evaluate the model's output quality. This assessment method not only considers whether the model's generated responses are accurate but also emphasizes feedback perception and the score on problem-solving relevance to ensure the model truly addresses user issues rather than merely generating seemingly reasonable responses. This detailed quality assessment helps enterprises make more informed decisions in the selection and optimization of models.

Fine-Tuning and Hallucination Control: The Value of Proprietary Data

To further enhance the performance of LLM models in specific enterprise scenarios, fine-tuning is an indispensable step. By using proprietary data to fine-tune the model, enterprises can significantly improve the model's accuracy and reliability in specific domains. However, a common issue during fine-tuning is "hallucinations" (i.e., the model generating incorrect or fictitious information). Therefore, enterprises need to assess the hallucination level in each given response and set confidence scores, applying these scores to the rest of the toolchain to minimize the number of hallucinations in the system.

This strategy not only improves the credibility of the model's output but also builds greater trust during user interactions, giving enterprises a competitive edge in the market.

Conclusion

Choosing and optimizing LLM models is a complex challenge that enterprises must face in their digital transformation journey. By considering NLP model training based on enterprise data and security, comprehensively evaluating inference quality and performance, and controlling hallucinations through fine-tuning, enterprises can achieve high-performing and highly customized LLM models while ensuring data security. This process not only enhances the enterprise's automation capabilities but also lays a solid foundation for success in a competitive market.

Through this discussion, it is hoped that readers will gain a clearer understanding of the key factors enterprises need to focus on when selecting and testing LLM models, enabling them to make more informed decisions in real-world applications.

HaxiTAG Studio is an enterprise-level LLM GenAl solution that integrates AIGC Workflow and privatization data fine-tuning.

Through a highly scalable Tasklets pipeline framework, flexible Al hub components, adpter, and KGM component, HaxiTAG Studio enables flexible setup, orchestration, rapid debugging, and realization of product POC. Additionally, HaxiTAG Studio is embedded with RAG technology solution and training data annotation tool system, assisting partners in achieving low-cost and rapid POC validation, LLM application, and GenAl integration into enterprise applications for quick verification and implementation.

As a trusted LLM and GenAl industry application solution, HaxiTAG provides enterprise partners with LLM and GenAl application solutions, private Al, and applied robotic automation to boost efficiency and productivity in applications and production systems. It helps partners leverage their data knowledge assets, integrate heterogeneous multi-modal information, and combine advanced Al capabilities to support fintech and enterprise application scenarios, creating value and growth opportunities.

HaxiTAG Studio, driven by LLM and GenAl, arranges bot sequences, creates feature bots, feature bot factories, and adapter hubs to connect external systems and databases for any function. HaxiTAG is a trusted solution for LLM and GenAl industry applications, designed to supply enterprise partners with LLM and GenAl application solutions, private Al, and robotic process automation to enhance efficiency and productivity. It helps partners leverage their data knowledge assets, relate and produce heterogeneous multimodal information, and amalgamate cutting-edge Al capabilities with enterprise application scenarios, creating value and development opportunities.

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Tuesday, September 23, 2025

Activating Unstructured Data to Drive AI Intelligence Loops: A Comprehensive Guide to HaxiTAG Studio’s Middle Platform Practices