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Wednesday, May 15, 2024

Google Gemini's GPT Search Update: Self-Revolution and Evolution

May 15, 2024

A New Era of AI-Driven Search: Google Gemini's Path to Innovation,Is it Google's fight to reinforce a search moat and avoid erosion of user scenarios and usage?

Since the inception of the Google search engine in 1998, the way we access and organize information on the internet has undergone a dramatic transformation. Twenty-five years later, powered by generative AI technology, Google has once again ushered us into a new information era with its latest customized Gemini model. At the recent I/O conference, Google showcased the new generation of search engines empowered by Gemini, demonstrating its formidable capabilities in understanding and handling complex queries, and providing solutions that traditional search engines could scarcely achieve. This article delves into this technological advancement and its transformative impact on future information retrieval, enterprise services, and productivity.

History and Development of Search Engines

Google's search engine initially leveraged techniques such as keyword matching and the PageRank algorithm to greatly enhance the efficiency of information retrieval, allowing users to quickly find the resources they needed online. However, with the explosive growth of internet content, user queries have become increasingly complex, presenting new challenges for traditional search engines in identifying and extracting valuable information from vast datasets.

Features of the Gemini Model

The introduction of the Gemini model signifies not only a breakthrough in generative AI for the search domain but also its remarkable capabilities in multimodal (such as text, images, and videos) and long-text processing. By combining deep learning and natural language processing (NLP) technologies, Gemini can understand and precisely answer complex user queries without requiring the user to break down their questions into multiple simple queries.

1. Multi-step Reasoning Capability

Gemini's multi-step reasoning capability highlights its advantage in handling complex problems. Users can pose queries with multiple details and considerations in one go, and Gemini can use logical reasoning to provide comprehensive and accurate answers. For instance, when planning a complex trip, users no longer need to search for information on different destinations or transportation methods individually; Gemini can integrate all relevant information and provide a complete travel plan.

2. Real-time Information and Context Awareness

In addition to static information, Gemini possesses real-time information processing and context awareness capabilities. This means users can instantly obtain current weather forecasts, traffic information, or other real-time dynamics during their search, enabling them to make more accurate decisions.

3. Integration with Enterprise Productivity Tools

Google demonstrated how Gemini enhances the intelligence of productivity tools like Workspace. For example, Gemini can automatically identify and parse multiple emails and their attachments, providing concise summaries and action items, significantly boosting work efficiency by eliminating the need for users to read and organize each email individually.

The Concept and Prospects of Large Model Agents

At the I/O conference, Google also introduced the concept of large model agents—intelligent systems capable of reasoning, planning, and memory. The advent of agents means AI can not only passively answer questions but also actively think and plan multi-step workflows. For example, Gemini can automatically summarize meeting notes and draft corresponding emails even in the user's absence, significantly reducing the likelihood of human error and greatly improving work efficiency.

The Future of Generative AI and Enterprise Services

The large-scale application of generative AI will further transform the mode of enterprise services. Google has demonstrated its leading edge through the customized Gemini model, especially in the comprehensive suite of applications known as the Google ecosystem, making it highly competitive in the enterprise service domain.

By promoting widespread AI adoption, enterprises can better understand customer needs, provide personalized services, and optimize internal workflows to reduce operational costs. For instance, in customer service, AI agents can provide real-time 24/7 responses, efficiently resolving customer issues; in market analysis, generative AI can offer deep market insights and forecasts through the analysis of vast datasets.

From the past simple information retrieval to today's comprehensive intelligent services, the evolution of Google's search engine and its underlying technology is undoubtedly a marvel in the history of internet development. With the application of the Gemini model, the AI-driven search experience will become smarter and more efficient, providing users with unprecedented convenience.

In the future, generative AI technology will not be limited to the search domain; it will undoubtedly permeate various industries, leading new industrial transformations. Through continuous innovation, Google is creating a smarter and more efficient era of information access and processing, opening a door to the future for global users.

Google Gemini: Advancing Intelligence in Search and Productivity Tools

May 16, 2024

At this year’s I/O conference, Google showcased its latest AI technology—Gemini. By integrating this customized large model, Google has not only revolutionized search engines but also empowered productivity tools, making them more intelligent and user-friendly. This article will delve into the innovative applications of Google Gemini in search engines and productivity tools, and its extensive commercial value.

Intelligent Search Engines: From Single Queries to Complex Solutions

Twenty-five years ago, Google’s search engine led the first wave of the information age by indexing and ranking internet information. Today, with the evolution of generative AI, the new generation of search engines powered by Gemini excels at understanding user needs, boasting contextual awareness, location sensitivity, and real-time information processing capabilities. Whether it’s simple Q&A or complex solutions, Gemini can swiftly provide the optimal answer.

The breakthrough in search engines lies in its multi-step reasoning capability, meaning users no longer need to break down complex questions into multiple searches. Gemini can handle these complex queries in one go, capturing every detail and consideration accurately. This capability demonstrates the remarkable advancements in AI technology in cognitive computing and data processing, significantly enhancing user experience.

For example, when planning a complex trip, one used to search for various details such as itinerary, accommodation, and transportation separately. Now, with a single query, Gemini provides a detailed and integrated plan, saving time and increasing efficiency.

Intelligent Upgrades in Productivity Tools

At this conference, Google also showcased Gemini’s applications in productivity tools like Google Workspace. Through its multimodal and long-text processing capabilities, Gemini can significantly enhance office automation. For instance, faced with a large volume of emails, users can request Gemini to summarize all emails from a school, including analyzing attached PDF files, and provide key points and action suggestions. This can greatly reduce the workload of information filtering, allowing users to focus on more valuable tasks.

Additionally, in remote meeting scenarios, Gemini demonstrated its outstanding voice processing and summarization capabilities. For example, if a user missed a one-hour Google Meet session, they could request Gemini to provide a summary of the meeting’s highlights, even suggesting specific actions. This intelligent support undoubtedly enhances collaboration efficiency and emergency response capabilities.

Prospects of Intelligent Systems with Large Model Agents

Google further showcased the broad application prospects of large model agents. Agents are not just tools but intelligent systems capable of reasoning, planning, and memory. Their application can preemptively "think" several steps ahead for users and seamlessly integrate different software and systems, further simplifying task execution. This cross-software and system working capability highlights AI technology’s immense potential in industrial applications.

For example, in project management, agents can help teams plan multiple steps in advance, including resource allocation, task distribution, and real-time monitoring. This not only improves work efficiency but also reduces the occurrence of human errors. The foresight and real-time response capabilities of agents are one of the core competencies of future intelligent office environments.

Competitive Advantage and Market Prospects

In the enterprise application space, Google clearly leads the way. The intelligent upgrades of its suite of applications give it a competitive edge in the market. While OpenAI and other companies are also actively advancing AI technology applications, Google’s user-friendliness and practicality are evidently superior.

It is foreseeable that with the large-scale deployment of Gemini, Google will gain significant competitive advantages in multiple business areas, enhancing user experience and significantly boosting enterprise productivity. Future search engines and productivity tools will not only be channels for information retrieval but also intelligent assistants helping us accomplish more complex tasks.

In summary, through Gemini, Google has demonstrated its innovation capabilities and commercial acumen in AI technology. Gemini not only revolutionizes traditional search engine functions but also brings intelligent transformations to productivity tools. These innovative applications will further promote the intelligent process of digital office and information retrieval, making AI technology a valuable assistant for both enterprises and individuals.

The New Era of AI-Driven Innovation

September 29, 2024

In today's rapidly evolving business landscape, Artificial Intelligence (AI) is profoundly transforming our work methods and innovation processes. As an expert in AI products and innovation, I am thrilled to introduce some cutting-edge AI-assisted tools and explore how they play crucial roles in innovation and decision-making. This article will delve into AI products such as ChatGPT, Claude, Poe, Perplexity, and Gemini, showcasing how they drive innovation and foster human-machine collaboration.

ChatGPT: A Powerful Ally in Creative Generation and Text Analysis

Developed by OpenAI, ChatGPT has gained renown for its exceptional natural language processing capabilities. It excels in creative generation, text analysis, and coding assistance, swiftly producing diverse ideas, aiding in copywriting, and solving programming challenges. Whether for brainstorming or executing specific tasks, ChatGPT provides invaluable support.

Claude: The Expert in Deep Analysis and Strategic Planning

Claude, created by Anthropic, stands out with its superior contextual understanding and reasoning abilities. It particularly shines in handling complex tasks and extended dialogues, making significant contributions in deep analysis, strategic planning, and academic research. For innovation projects requiring profound insights and comprehensive thinking, Claude offers forward-looking and strategic advice.

Poe: A Platform Integrating Multiple Models

As a platform integrating various AI models, Poe offers users the flexibility to choose different models. This diversity makes Poe an ideal tool for tackling various tasks and comparing the effectiveness of different models. In the innovation process, Poe allows teams to leverage the unique strengths of different models, providing multi-faceted solutions to complex problems.

Perplexity: The New Trend Combining AI with Search Engines

Perplexity represents the emerging trend of combining AI with search engines. It provides real-time, traceable information, particularly suitable for market research, competitive analysis, and trend insights. In the fast-paced innovation environment, Perplexity can swiftly gather the latest market dynamics and industry information, offering timely and reliable data support for decision-makers.

Gemini: The Pioneer of Multimodal AI Models

Google's latest multimodal AI model, Gemini, demonstrates exceptional ability in processing various data types, including text and images. It excels in complex scenario analysis and multimedia content creation, capable of handling challenging tasks such as visual creative generation and cross-media problem analysis. Gemini's multimodal features bring new possibilities to the innovation process, making cross-disciplinary innovation more accessible.

Building a Robust Innovation Ecosystem

These AI tools collectively construct a powerful innovation ecosystem. By integrating their strengths, organizations can comprehensively enhance their innovation capabilities, improve decision quality, accelerate innovation cycles, explore new innovation frontiers, and optimize resource allocation. A typical AI-assisted innovation process might include the following steps:

Problem Definition: Human experts clearly define innovation goals and constraints.
AI-Assisted Research: Utilize tools like Perplexity for market research and data analysis.
Idea Generation: Use ChatGPT or Claude to generate initial innovative solutions.
Human Evaluation: Expert teams assess AI-generated proposals and provide feedback.
Iterative Optimization: Based on feedback, use tools like Gemini for multi-dimensional optimization.

Wise AI Product Selection Strategy

To maximize the benefits of AI tools, organizations need to formulate a prudent AI product selection strategy:

Choose the most suitable AI tools based on task complexity and characteristics.
Fully leverage the advantages of different AI tools to optimize the decision-making process.
Encourage human experts to become proficient users and coordinators of AI tools.

Through this approach, organizations can maintain the core position of human creativity and judgment while fully harnessing the advantages of AI technology, achieving a more efficient and effective innovation process.

The Future Path of Innovation

AI technology is rapidly evolving, with new tools and models constantly emerging. Therefore, staying abreast of the latest developments in the AI field and flexibly adjusting application strategies is crucial for maintaining innovation advantages. AI products like ChatGPT, Claude, Poe, Perplexity, and Gemini are reshaping innovation processes and decision-making methods. They are not just powerful auxiliary tools but keys to unlocking new thinking and possibilities. By wisely integrating these AI tools, organizations can build a more efficient, flexible, and innovative work environment, maintaining a leading position in the competitive market. Future success will belong to those organizations that can skillfully balance human wisdom with AI capabilities.

Apple Intelligence: Redefining the Future of Personal Intelligent Systems

June 11, 2024

Analysis and Commentary: AI Product Developer Program Announced at Apple WWDC

At the latest Apple WWDC, Apple announced a new AI product developer program, unveiling a system called "Apple Intelligence." This technology not only elevates the level of personal intelligent systems but also opens new possibilities for enterprise services and technological innovation. This article analyzes the significance of Apple Intelligence from multiple perspectives and its impact on technology and solution providers.

1. Core Capabilities of Apple Intelligence

Apple Intelligence is a new personal intelligent system, akin to LLM as OS, with the following core capabilities:

Basic LLM Cross-System Toolbar Queries: Capable of handling text, images, and other content through a system-level toolbar.
Perceiving Personal Context: Intelligently perceives the user's context by referencing screen content, emails, calendars, semantic search information, notifications, contacts, etc.
Action Execution: Executes operations directly based on contextual information, such as sending messages and planning navigation.

These combined capabilities make Apple Intelligence an extremely powerful system, capable of understanding and responding to complex user needs. For example, if a user’s meeting time changes, Apple Intelligence can intelligently assess whether it will affect attending other scheduled activities by considering meetings, traffic, and other schedules.

2. System-Level Context Perception and Cross-App Actions

A standout feature of Apple Intelligence is its system-level context perception and cross-app actions. This deep integration is unparalleled by other platforms. Apple illustrated the importance of this capability by showing how it can intelligently make decisions based on multiple sources of information, such as the impact of rescheduled meetings on other appointments.

3. Private Cloud Compute Technology

Apple Intelligence prioritizes local and privacy security, utilizing local end-side LLM and providing Private Cloud Compute technology. This ensures that data is not stored but only used to execute requests, greatly enhancing user data privacy protection. It also supports the introduction of server models like GPT-4o and Gemini for handling more complex needs. This multi-level model support combines the advantages of local and cloud computing, providing users with safer and more efficient services.

4. Comprehensive Upgrade of Siri

Based on Apple Intelligence, Siri has undergone a comprehensive upgrade, supporting interaction through typing or voice and intelligently perceiving screen content. Whether handling messages, images, or conducting semantic indexing and OCR operations, Siri demonstrates enhanced functionality. This upgrade transforms Siri from a simple voice assistant into a multifunctional intelligent assistant, significantly improving the user experience.

5. Impact on Developers and Solution Providers

Apple Intelligence opens multiple entry points for developers, such as Image Playground and Writing Tools, supporting developers in creating more innovative applications. This not only provides developers with more creative space but also drives the development of the entire AI ecosystem.

Apple Intelligence redefines the standard of personal intelligent systems through system-level context perception and cross-app actions. Its prioritization of privacy-secure local computing combined with Private Cloud Compute provides users with more powerful functions and higher privacy protection. Additionally, the openness of Apple Intelligence offers new opportunities for developers and technology providers, driving further advancement in AI technology. In summary, the release of Apple Intelligence marks the beginning of a new era for personal intelligent systems.

TAGS:

Apple Intelligence personal assistant, AI product developer program, Apple WWDC AI announcement, LLM as OS system, system-level context perception, cross-app action execution, Private Cloud Compute technology, Siri comprehensive upgrade, privacy-secure local computing, AI ecosystem development

LLMs Enter Enterprise Core Systems — The Real Question Is No Longer "Is the Model Strong Enough?"

May 08, 2026

In the past two years, enterprise AI infrastructure has undergone a distinct transformation.

Enterprises no longer lack models.

From OpenAI, Anthropic, Google Gemini to DeepSeek, vLLM, SGLang, and Ollama, model capabilities and inference performance are evolving rapidly. Yet, once enterprises enter real production environments, they begin confronting another set of more pragmatic challenges:

AI answers "look correct" but cannot prove their basis;
Different models exhibit vast capability disparities, making business systems increasingly difficult to maintain;
Enterprise knowledge is scattered across documents, databases, emails, and audio-visual content, unable to coalesce into a unified understanding;
Inference costs, model routing, data security, and protocol compatibility gradually become new sources of system complexity;
Enterprises have already adopted AI, yet still cannot truly "trust AI in production."

This is precisely why Yueli KGM Computing is now open-source.

It is an enterprise production-grade AI application framework.

More accurately, it is:

The "knowledge computation and inference orchestration infrastructure layer" for the enterprise AI application era.

What Is Yueli KGM Computing?

An "Inference Orchestration + Compatible Gateway + Knowledge Computation" Middleware for Enterprise AI

Yueli KGM Computing is an open-source, enterprise-grade knowledge computation engine and inference orchestration middleware.

Its core positioning is unequivocal:

Use the determinism of knowledge graphs to constrain the probabilistic nature of large language models.

It doesn't seek to "make models smarter."

Instead, it addresses:

How to make enterprise AI more trustworthy;
How to make multi-model systems governable;
How to truly embed inference capabilities into enterprise business systems;
How to equip AI infrastructure with observability, replaceability, and auditability.

It can serve as:

An OpenAI / Anthropic compatible gateway;
A multi-model routing and scheduling layer;
An enterprise knowledge graph and GraphRAG engine;
A privatized AI infrastructure control plane;
An enterprise AI middleware embedded into existing systems.

It can also:

Connect to local vLLM / Ollama / SGLang;
Integrate with OpenAI-compatible cloud services;
Orchestrate a hybrid of local inference and cloud MaaS;
Deliver model governance and knowledge augmentation under a unified API gateway and scheduling controller.

Why Does Enterprise AI Need a "Knowledge Computation Layer"?

For many enterprise AI projects today, the real problem is not model performance.

It is this:

Enterprise Knowledge Is Not Entering the Inference Pipeline

The problem with traditional RAG is:

Retrieval results are merely "similar text";
They lack relational structures;
They lack domain ontologies;
They lack factual boundaries;
They lack source verifiability.

The result:

The model generates a wrong answer that "looks exactly like the right answer."

In industries such as finance, healthcare, government, manufacturing, new energy, intellectual property, and compliance, such problems are unacceptable.

Therefore, the core capability of Yueli KGM Computing is not simple vector retrieval.

It is:

KGM (Knowledge Generation Modeling)

That is:

An LLM Inference System Constrained by Knowledge Graphs

It will:

Extract entities and relationships from enterprise documents, databases, audio-visual content, and business systems;
Construct an enterprise private domain ontology;
Organize knowledge into a reasonable graph;
Perform GraphRAG retrieval before inference;
Inject factual nodes as constraint context into the LLM;
Output traceable, verifiable results.

This means:

AI is no longer "freestyling."

Instead:

It performs controlled reasoning within the boundaries of enterprise knowledge.

What Does Yueli KGM Computing Actually Deliver?

A Unified Industrial Protocol AI Gateway Layer

Within the same process, KGM simultaneously provides:

OpenAI Compatible API
Anthropic Claude Compatible API

Including:

/v1/chat/completions
/v1/responses
/v1/messages

And automatically completes:

tool_calls
tool_use

Dual-protocol semantic mapping.

This means:

Enterprise applications only need to connect to a single Base URL.

No matter how the underlying models change, business systems remain agnostic.

Dynamic Inference Orchestration and Model Scheduling

KGM supports:

Local inference;
Cloud MaaS;
Multi-model hybrid scheduling;
Cost-based scheduling;
Performance-based scheduling;
Dynamic routing by task type.

For example:

Sensitive data → On-premise Ollama;
Long text → Gemini;
Highly complex reasoning → Claude;
High throughput → vLLM;
Low cost → DeepSeek.

All of this can be accomplished through declarative configuration.

Rather than rewriting a routing layer for every project.

Knowledge Graph-Driven GraphRAG

This is KGM's most central capability.

Compared to traditional vector RAG:

KGM constructs:

Enterprise domain ontology;
Relationship graphs;
Contextual reasoning paths;
Structured factual constraints.

Therefore, it not only knows:

"Which texts are similar."

It also knows:

"What relationships exist among pieces of knowledge."

This is the critical leap for enterprise AI from "chat tool" to "business system."

Enterprise-Grade Control Plane and Observability

After going live, a significant number of AI projects rapidly descend into an "ungovernable state."

Enterprises find themselves unable to answer:

Which model is providing the service?
Which requests are the most costly?
Which inference node is failing?
Which API has abnormal latency?
Which model has a higher hallucination rate?

KGM provides:

Prometheus Metrics;
Runtime lifecycle management;
Circuit breaker mechanisms;
Structured logging;
Model asset governance;
Runtime control plane;
Multi-tenant isolation;
Data security policies.

It is not a simple proxy.

It is a genuinely operable AI middleware.

How Do Enterprises Embed Yueli KGM?

Scenario One: Enterprise Knowledge Q&A

The typical path:

Enterprise Documents / Databases / Wikis / Emails
                    ↓
            KGM Semantic Parsing
                    ↓
          GraphRAG Knowledge Graph
                    ↓
            LLM Constrained Inference
                    ↓
        Traceable, Trustworthy Answers

R&D teams no longer depend on:

"Who remembers the solution from back then?"

Instead, they directly ask:

In which version did this issue appear?
How was it fixed at the time?
Which systems were affected?
Who was involved in the decision?

KGM will construct a complete knowledge chain from:

Git;
Confluence;
Emails;
Meeting records;
Technical documentation.

Scenario Two: Finance and Compliance Review

The biggest risk with traditional LLMs:

Citing non-existent regulations.

KGM's approach is:

Build a regulatory knowledge graph;
Structure regulatory clauses;
Restrict reasoning within knowledge boundaries;
Directly trigger a "knowledge gap" alert beyond those boundaries.

This means:

AI no longer "guesses."

It reasons within the enterprise's rule system.

Scenario Three: AI-Native Product Embedding

For engineering teams:

KGM can serve as the underlying AI Runtime.

Including:

Multi-model scheduling;
GraphRAG;
Tool Calling;
MCP;
Memory;
Knowledge Runtime;
Prompt orchestration;
Runtime Observability.

Engineering teams no longer need to rebuild:

Gateways;
Routing;
Metrics;
Tool Runtime;
Protocol adaptation;
Multi-model compatibility layers.

Scenario Four: Audio-Visual Semantic Computing

This is a direction often overlooked by enterprises today but is exceptionally high-value.

KGM supports:

Video caption parsing;
Semantic label extraction;
Meeting content knowledge transformation;
Training video knowledge graphs;
Audio-visual Q&A.

For example:

An enterprise can directly ask:

"In last quarter's product meetings, what were the disputes regarding pricing strategy?"

The system will automatically locate:

The corresponding meeting;
The corresponding individuals;
The corresponding viewpoints;
The corresponding timeline.

What Is Its Relationship to LangChain, LlamaIndex, and vLLM?

This is not a competitive relationship.

Rather, it is:

A Layered Relationship

Layer	Representative Project	Core Responsibility
Inference	vLLM / SGLang	High-performance inference
Application	LangChain / Dify	Agent and Workflow
Data	LlamaIndex	Data connection and retrieval
Middleware	Yueli KGM	Inference orchestration + Protocol compatibility + Knowledge constraints

Therefore, the most rational enterprise architecture often is:

vLLM for inference;
LangChain for business agents;
Dify or BotFactory for low-code workflows;
KGM as the unified AI middleware and knowledge computation layer.

Why MIT Open Source?

The Yueli KGM Computing GitHub Repository and NPM package are open-sourced under the MIT License.

This means:

Enterprises can use it freely for commercial purposes;
They can modify it for private deployment;
They can deeply integrate it;
They can build their own industry-specific versions.

The true value of Yueli KGM Computing does not lie in closed-source code.

It lies in:

Enterprise AI infrastructure capability;
Industry knowledge modeling experience;
Private deployment delivery capability;
Knowledge engineering systems;
Data intelligence and inference architecture practices.

The Next Phase of Enterprise AI Is Shifting from "Model Competition" to "Knowledge Governance"

Over the past two years, the industry has been discussing:

Whose model is stronger.

But in the next five years, the questions enterprises will truly care about will become:

Who can make AI more trustworthy?
Who can make AI more stable?
Who can make AI truly enter business systems?
Who can equip AI with enterprise-grade governance capabilities?

The significance of Yueli KGM Computing lies precisely here.

It is a crucial middleware layer for enterprise AI transitioning from the experimental stage to production-grade infrastructure.

Four Strategic Steps for AI-Driven Procurement Transformation: Maturity Assessment, Buy-or-Build Decision, Capability Enablement, and Value Capture

July 31, 2025

Four Strategic Steps for AI-Driven Procurement Transformation: Maturity Assessment, Buy-or-Build Decision, Capability Enablement, and Value Capture

Integrating Artificial Intelligence (AI) into procurement is not a one-off endeavor, but a structured journey that requires four critical stages. These are: conducting a comprehensive digital maturity assessment, making strategic decisions on whether to buy or build AI solutions, empowering teams with the necessary skills and change management, and continuously capturing financial value through improved data insights and supplier negotiations. This article draws from leading industry practices and the latest research to provide an in-depth analysis of each stage, offering procurement leaders a practical roadmap for advancing their AI transformation initiatives with confidence.

Digital Maturity Assessment

Before embarking on AI adoption, organizations must first evaluate their level of digital maturity to accurately identify current pain points and future opportunities. AI maturity models offer procurement leaders a strategic framework to map out their current state across technological infrastructure, team capabilities, and the digitization of procurement processes—thereby guiding the development of a realistic and actionable transformation roadmap.

According to McKinsey, a dual-track approach is essential: one track focuses on implementing high-impact, quick-win AI and analytics use cases, while the other builds a scalable data platform to support long-term innovation. Meanwhile, DNV’s AI maturity assessment methodology emphasizes aligning AI ambitions with organizational vision and industry benchmarks to ensure clear prioritization and avoid isolated, siloed technologies.

Buy vs. Build: Technology Decision-Making

A pivotal question facing many organizations is whether to purchase off-the-shelf AI solutions or develop customized systems in-house. Buying ready-made solutions often enables faster deployment, provides user-friendly interfaces, and requires minimal in-house AI expertise. However, such solutions may fall short in meeting the nuanced and specialized needs of procurement functions.

Conversely, organizations with higher AI ambitions may prefer to build tailored systems that deliver deeper visibility into spending, contract optimization, and ESG (Environmental, Social, and Governance) alignment. This route, however, demands strong internal capabilities in data engineering and algorithm development, and requires careful consideration of long-term maintenance costs versus strategic benefits.

As Forbes highlights, successful AI implementation depends not only on technology, but also on internal trust, ease of use, and alignment with long-term business strategy—factors often overlooked in the buy-vs.-build debate. Initial investment and ongoing iteration costs should also be factored in early to ensure sustainable returns.

Capability Enablement and Team Empowerment

AI not only accelerates existing procurement workflows but also redefines them. As such, empowering teams with new skills is crucial. According to BCG, only 10% of AI’s total value stems from algorithms themselves, while 20% comes from data and platforms—and a striking 70% is driven by people’s ability to adapt to and embrace new ways of working.

A report by Economist Impact reveals that 64% of enterprises already use AI tools in procurement. This shift demands that existing employees develop data analysis and decision support capabilities, while also incorporating new roles such as data scientists and AI engineers. Leadership must champion change management, foster open communication, and create a culture of experimentation and continuous learning to ensure skills development is embedded in daily operations.

Hackett Group emphasizes that the most critical future skills for procurement teams include advanced analytics, risk assessment, and cross-functional collaboration—essential for navigating complex negotiations and managing supplier relationships. Supply Chain Management Review also notes that AI empowers resource-constrained organizations to "learn by doing," accelerating hands-on mastery and fostering a mindset of continuous improvement.

Capturing Value from Suppliers

The ultimate goal of AI in procurement is to deliver measurable business value. This includes enhanced pre-negotiation insights through advanced data analytics, optimized contract terms, and even influencing suppliers to adopt generative AI (GenAI) technologies to reduce costs across the supply chain.

BCG’s research shows that organizations undertaking these four transformation steps can achieve cost savings of 15% to 45% in select product and service categories. Success hinges on deeply embedding AI into procurement workflows and delivering a compelling initial user experience to foster adoption and scale. Sustained value creation also requires strong executive sponsorship, with clear KPIs and continuous promotion of success stories to ensure AI becomes a core driver of long-term enterprise growth.

Conclusion

In today’s fiercely competitive landscape, AI-powered procurement transformation is no longer optional—it is imperative. It serves as a vital lever for gaining future-ready advantages and building core competitive capabilities. Backed by structured maturity assessments, precise technology decisions, robust capability building, and sustainable value capture, the Hashitag team stands ready to support your procurement organization in navigating the digital tide and achieving intelligent transformation. We hope this four-step framework provides clarity and direction as your organization advances toward the next era of procurement excellence.

HaxiTAG Intelligent Application Middle Platform: A Technical Paradigm of AI Intelligence and Data Collaboration

October 31, 2024

In the context of modern enterprise AI applications, the integration of data and AI capabilities is crucial for technological breakthroughs. Under the framework of the HaxiTAG Intelligent Application Middle Platform, we have developed a comprehensive supply chain and software ecosystem for Large Language Models (LLMs), aimed at providing efficient data management and inference capabilities through the integration of knowledge data, local data, edge-hosted data, and the extended data required for API-hosted inference.

Integration of LLM Knowledge Data

The core of LLMs lies in the accumulation and real-time integration of high-quality knowledge data. The HaxiTAG platform continuously optimizes the update processes for knowledge graphs, structured, and unstructured data through efficient data management workflows and intelligent algorithms, ensuring that models can perform accurate inference based on the latest data. Dynamic data updates and real-time inference are fundamental to enhancing model performance in practical applications.

Knowledge Integration of Local Data

A key capability of the HaxiTAG platform is the seamless integration of enterprise local data with LLM models to support personalized AI solutions. Through meticulous management and optimized inference of local data, HaxiTAG ensures that proprietary data is fully utilized while providing customized AI inference services for enterprises, all while safeguarding privacy and security.

Inference Capability of Edge-hosted Data

To address the demands for real-time processing and data privacy, the HaxiTAG platform supports inference on "edge"-hosted data at the device level. This edge computing configuration reduces latency and enhances data processing efficiency, particularly suited for industries with high requirements for real-time performance and privacy protection. For instance, in industrial automation, edge inference can monitor equipment operating conditions in real time and provide rapid feedback.

Extended Data Access for API-hosted Inference

With the increasing demand for API-hosted inference, the HaxiTAG platform supports model inference through third-party APIs, including OpenAI, Anthropic, Qwen, Google Gemini, GLM, Baidu Ernie, and others, integrating inference results with internal data to achieve cross-platform data fusion and inference integration. This flexible API architecture enables enterprises to rapidly deploy and optimize AI models on existing infrastructures.

Integration of Third-party Application Data

The HaxiTAG platform facilitates the integration of data hosted by third-party applications into algorithms and inference workflows through open APIs and standardized data interfaces. Whether through cloud-hosted applications or externally hosted extended data, we ensure efficient data flow and integration, maximizing collaborative data utilization.

Key Challenges in Data Pipelines and Inference

In the implementation of enterprise-level AI, constructing effective data pipelines and enhancing inference capabilities are two critical challenges. Data pipelines encompass not only data collection, cleansing, and storage, but also core requirements such as data privacy, security, and real-time processing. The HaxiTAG platform leverages automation and data governance technologies to help enterprises establish a continuous integration DevOps data pipeline, ensuring efficient data flow and quality control.

Collaboration Between Application and Algorithm Platforms

In practical projects, the collaboration between application platforms and algorithm platforms is key to enhancing model inference effectiveness. The HaxiTAG platform employs a distributed architecture to achieve efficiency and security in the inference process. Whether through cloud-scale inference or local edge inference, our platform can flexibly adjust inference configurations based on business needs, thereby enhancing the AI application capabilities of enterprises.

Practical Applications and Success Cases

In various industry practices, the HaxiTAG platform has successfully demonstrated its collaborative capabilities between data and algorithm platforms. For instance, in industrial research, HaxiTAG optimized the equipment status prediction system through automated data analysis processes, significantly improving production efficiency. In healthcare, we constructed knowledge graphs and repositories to assist doctors in analyzing complex cases, markedly enhancing diagnostic efficiency and accuracy.

Additionally, the security and compliance features of the HaxiTAG platform ensure that data privacy is rigorously protected during inference processes, enabling enterprises to effectively utilize data for inference and decision-making while meeting compliance requirements.

Contact

Wednesday, May 15, 2024

History and Development of Search Engines

Features of the Gemini Model

1. Multi-step Reasoning Capability

2. Real-time Information and Context Awareness

3. Integration with Enterprise Productivity Tools

The Concept and Prospects of Large Model Agents

The Future of Generative AI and Enterprise Services

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Thursday, May 16, 2024

Intelligent Search Engines: From Single Queries to Complex Solutions

Intelligent Upgrades in Productivity Tools

Prospects of Intelligent Systems with Large Model Agents

Competitive Advantage and Market Prospects

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Sunday, September 29, 2024

ChatGPT: A Powerful Ally in Creative Generation and Text Analysis

Claude: The Expert in Deep Analysis and Strategic Planning

Poe: A Platform Integrating Multiple Models

Perplexity: The New Trend Combining AI with Search Engines

Gemini: The Pioneer of Multimodal AI Models

Building a Robust Innovation Ecosystem

Wise AI Product Selection Strategy

The Future Path of Innovation

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Tuesday, June 11, 2024

Analysis and Commentary: AI Product Developer Program Announced at Apple WWDC

1. Core Capabilities of Apple Intelligence

2. System-Level Context Perception and Cross-App Actions

3. Private Cloud Compute Technology

4. Comprehensive Upgrade of Siri

5. Impact on Developers and Solution Providers

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Friday, May 8, 2026

What Is Yueli KGM Computing?

An "Inference Orchestration + Compatible Gateway + Knowledge Computation" Middleware for Enterprise AI

Why Does Enterprise AI Need a "Knowledge Computation Layer"?

Enterprise Knowledge Is Not Entering the Inference Pipeline

An LLM Inference System Constrained by Knowledge Graphs

What Does Yueli KGM Computing Actually Deliver?

A Unified Industrial Protocol AI Gateway Layer

Dynamic Inference Orchestration and Model Scheduling

Knowledge Graph-Driven GraphRAG

Enterprise-Grade Control Plane and Observability

How Do Enterprises Embed Yueli KGM?

Scenario One: Enterprise Knowledge Q&A

Scenario Two: Finance and Compliance Review

Scenario Three: AI-Native Product Embedding

Scenario Four: Audio-Visual Semantic Computing

What Is Its Relationship to LangChain, LlamaIndex, and vLLM?

A Layered Relationship

Why MIT Open Source?

The Next Phase of Enterprise AI Is Shifting from "Model Competition" to "Knowledge Governance"

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Thursday, July 31, 2025

Digital Maturity Assessment

Buy vs. Build: Technology Decision-Making

Capability Enablement and Team Empowerment

Capturing Value from Suppliers

Conclusion

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Thursday, October 31, 2024

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