AI Search: A new-generation enterprise-level knowledge engine that enables enterprise knowledge to be "understood, applicable, and capable of creation"

Enterprises have long been plagued by data management dilemmas including scattered information, failure to understand user intents during search, and the need for manual screening and analysis even after locating required materials, which leads to low efficiency in cross-system processing. Traditional keyword search mainly focuses on "finding content", while simple RAG Q&A primarily solves the problem of "generating answers", neither of which can support understanding, analysis and execution in complex business scenarios. What enterprises truly need is a new-generation intelligent platform that can connect enterprise data, understand business contexts, integrate knowledge and assist in problem-solving.

AI Search - A New-Generation Enterprise Knowledge Engine 

AI Search is a new-generation intelligent search platform launched by Datagrand. It is neither a general large model search tool nor a simple RAG Q&A robot, but a real enterprise-level knowledge engine that enables enterprise knowledge to be "understood, applicable, and creative" — it acts as a unified knowledge search entry to break through the limitations of global search within the enterprise.

 Built on the permission-aware knowledge graph technology as its core foundation, AI Search reconstructs enterprise knowledge indexes and builds enterprise context capabilities covering connectors, enterprise graphs and permission inheritance. Through deep personalization, it constructs personal graphs for each employee to understand their tasks, projects, collaborators and unique working styles, making search results truly "understand what you need".

 Based on the Core Concept of "Locate → Understand → Execute" 

Based on the core concept of "Locate → Understand → Execute", AI Search can accurately capture users' real intentions, automatically judge the complexity of questions, and dynamically select the optimal search strategy; it integrates internal enterprise knowledge, business system data and internet information, and outputs accurate, credible and executable answers through multi-source retrieval, intelligent reasoning and Agent collaboration.  

By connecting enterprise data assets, precipitating business knowledge capabilities and opening intelligent service interfaces, AI Search helps enterprises build a new-generation intelligent entry for employees, customers and business scenarios, making every search a starting point for decision-making.

 AI Search Focuses on Solving 6 Major Business Problems 

1. Information cannot be found, cannot be accurately located, or takes too long to retrieve 

Through intent understanding, semantic retrieval and multi-way recall, users can find truly relevant information faster, reducing repeated searches and manual screening.

 2. Enterprise data is scattered, making knowledge difficult to utilize in a unified manner 

Connect document libraries, business systems, databases and external data, and convert scattered information into unified, searchable and understandable enterprise knowledge. 

3. Manual analysis is still required after finding materials. Through multi-source retrieval, content summarization, information comparison and intelligent reasoning, it helps users directly obtain conclusions, basis and key risk points. 

4. Complex business processes rely excessively on manual experience 

Integrate historical cases, business rules and expert knowledge to assist in risk identification, matter judgment, reply generation and problem troubleshooting.

5. Enterprise knowledge is difficult to precipitate and reuse 

Precipitate personal experience and historical processing results into organizational knowledge, and promote continuous reuse of knowledge across different personnel, departments and business scenarios. 

6. Intelligent capabilities are difficult to integrate into existing business systems 

Open search, knowledge, Agent and data capabilities through APIs, SDKs, Skills, MCP and other methods to quickly embed into existing systems and business processes. 

7 Core Capabilities of AI Search 

With the knowledge engine as the core, the permission-aware knowledge graph as the foundation, and 7 core capabilities as support, AI Search helps enterprises truly realize the leap of knowledge from "being searched" to "being understood, applied and created". 

Connect enterprise data assets, precipitate business knowledge capabilities, open intelligent service interfaces — AI Search makes every search a starting point for decision-making. 

1 Unified Intelligent Entry: From "Finding Information" to "Solving Problems" 

Provide end users with a unified intelligent search entry, offering multi-mode experiences including search, Q&A, and intelligent agents. Through Query understanding, semantic analysis and multi-turn interaction capabilities, it helps users quickly express their needs and obtain accurate answers. 

Supported functions: AI Search intelligent search, Chat intelligent Q&A, Agent intelligent task execution, expert group collaboration, and user profile-based personalized recommendation for different users. 

2 Operable and Optimizable Search Capabilities 

Provide full-link configuration capabilities for search, including search effect operation and strategy configuration. Through continuous operation, business teams can continuously optimize the search experience without relying on R&D teams. 

Supported functions: dictionary management (synonyms, proprietary words, error correction words, sensitive words, stop words), Query understanding and rewriting, intent recognition configuration, recall strategy configuration, sorting strategy optimization, black and white list intervention, search effect analysis. 

3 Unified Intelligent Foundation to Quickly Build Industry Intelligent Applications 

Enterprises can quickly convert search capabilities into scenario-based applications according to their own business needs. Enterprises do not need to rebuild underlying search capabilities, and can quickly deploy intelligent services only by configuring applications according to business requirements. 

4 Connect Multi-source Enterprise Data to Build a Unified Knowledge Entry 

Provide enterprise data access, processing and management capabilities, uniformly process structured and unstructured data, and convert information scattered in different systems into searchable and understandable data assets. 

Supported functions: document library access, business system connection, database access, third-party data access, network information access. Figure 5 Unified knowledge entry supporting multi-dimensional data access

5 Integrate Various Models Such as LLM/Machine Learning to Form a Closed Loop of Data-Model-Application 

Provide unified management capabilities for large models and small models required for search and intelligent applications, support model neutrality, and customers can freely choose the optimal model combination. Supported functions: model access, model training, model evaluation, intent recognition model, Query rewriting model, sorting model. 

6 Open Intelligent Capability Ecosystem: From Capability Opening to Shared Enterprise Intelligent Ecosystem 

Open platform capabilities through APIs, SDKs, Skills, MCP and other methods, helping enterprises quickly integrate AI Search platform capabilities into existing business systems and intelligent agent platforms to realize capability reuse and ecological expansion. 

Supported functions: search capability invocation, knowledge capability invocation, Agent capability invocation, data capability invocation. 

7 Intelligent Q&A and Usage Guidance: Proactive product Q&A, guidance for every step of use

 Provide users with product usage guidance, business problem answers and intelligent service support to reduce user learning costs and improve platform usage experience. Through intelligent guidance, new users can get started quickly, and existing users can continuously discover the value of the platform. 

Application Scenarios of AI Search 

• AI Search is widely applicable to the following business scenarios: 
• Unified enterprise knowledge search and intelligent employee assistant; 
• Financial risk supervision and risk event analysis; 
• Intelligent reply for petition, complaint and customer service; 
• Regulation and policy retrieval and compliance review; 
• Investment research analysis and business decision support; 
• Operation anomaly troubleshooting and business problem positioning; 
• Document comparison, material review and intelligent task execution.

 In-depth Analysis of Typical Scenarios 

1 Intelligent Financial Risk Supervision Platform 

Build a regional financial risk perception and disposal system based on AI search and knowledge fusion capabilities. 

For financial regulatory authorities, the AI Search platform builds an enterprise risk knowledge system by connecting multi-source data such as enterprise ledgers, OA systems, regulatory documents, and historical events. The platform supports financial enterprise profiling, risk event retrieval, and regional risk situation analysis. It realizes intelligent risk discovery, risk research and judgment, and risk disposal by understanding enterprise information, associating historical data, and extracting risk factors through AI. 

Combined with the financial risk map, the visualization of regional financial risk distribution is realized; combined with the intelligent search capability, it helps regulators quickly query enterprise information, trace risk basis and assist decision-making. 

Core value: Integrate scattered financial regulatory data to form an enterprise risk ledger; identify potential risks based on AI understanding capabilities, improve the efficiency of risk discovery and disposal, and support the digital upgrading of financial supervision. Figure 8 Intelligent Financial Risk Supervision Platform 

2 Intelligent Petition Reply Assistant 

Realize intelligent identification and standardized disposal of petition matters based on AI search and knowledge base capabilities. 

Aiming at the problems of large number of petitions, high professional requirements and strict reply standards in the field of financial supervision, the AI Search platform combines knowledge resources such as historical petition cases, policies and regulations, and business rules to create an intelligent petition processing assistant. The system can automatically understand the content of complaints, identify the type of matters, extract key elements such as enterprises, demands, and risks, and recommend standard reply plans based on historical cases and policy basis. Through the knowledge closed loop of "use - precipitate - update", the petition knowledge base is continuously optimized to make it more accurate with more usage. 

Core value: Automatically identify petition matters to improve sorting efficiency; quickly match historical cases and policy basis, recommend standardized reply templates to reduce labor costs; unify reply standards to reduce compliance risks.