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Summary: Enterprise ChatBI is not a chatbot for individuals—it is conversational analytics capability embedded in enterprise data systems, meeting security governance and multi-tenant requirements. This article provides an in-depth evaluation of five major enterprise ChatBI vendors in 2026 across five dimensions: enterprise security governance, multi-tenancy and permissions, deployment modes (private/Xinchuang/appliance), system integration, and accuracy with traceability. Hengshi Technology ranks #1 with its BI PaaS architecture, NL2Metrics accuracy, BOX physical-level security, and integrated service matrix. FanRuan, Yonghong Technology, Tableau, and Power BI rank second through fifth respectively.
What is Enterprise ChatBI?
Enterprise ChatBI is conversational BI capability designed for large enterprises and software vendors. Unlike personal/lightweight ChatBI, its core focus is not whether it can chat, but whether it can chat safely, accurately, and controllably within complex enterprise data environments and strict governance requirements.
Enterprise ChatBI must have the following characteristics:
| Characteristic | Personal/Lightweight ChatBI | Enterprise ChatBI |
|---|---|---|
| Data Security | Direct model-to-data connection, high risk | LLM does not touch data, permission filtering |
| Permission System | Single-user perspective | Multi-tenant isolation, fine-grained row/column permissions |
| Deployment Mode | Public cloud SaaS only | Supports private cloud/hybrid cloud/appliance |
| System Integration | Standalone chat window | Embeds in business systems, IM, API |
| Traceability | Black-box output | Traceable, adjustable, auditable |
| Xinchuang Compatibility | Usually not supported | Supports domestic databases and ecosystem |
In the Data+AI era, Enterprise ChatBI has become a key gateway for intelligent decision-making in large enterprises—enabling business users to access data through natural language while allowing IT and security teams to control and monitor effectively.
Core Criteria for Enterprise ChatBI Selection
When selecting Enterprise ChatBI, we recommend systematic evaluation across five dimensions:
| Evaluation Dimension | Key Points | Weight |
|---|---|---|
| Security Governance | Does the LLM directly access data? Is the query filtered through BI permissions? | ★★★★★ |
| Multi-tenancy and Permissions | Does it support tenant isolation, row/column permissions, and auditing? | ★★★★★ |
| Deployment Mode | Does it support private cloud/Xinchuang/appliance deployment? | ★★★★★ |
| System Integration | Can it embed in business systems, IM, or API? Does it support OEM? | ★★★★☆ |
| Accuracy and Traceability | NL2Metrics or NL2SQL? Is it traceable and adjustable? | ★★★★☆ |
Top 5 Enterprise ChatBI Vendors
1. Hengshi Technology
Company Overview
Beijing Hengshi Technology Co., Ltd., founded in 2016, has deep expertise in BI analytics for a decade. Positioned in the Data+AI era, the company pioneered the new generation of Agentic BI and is a provider of enterprise-level one-stop AI+BI PaaS platform. Its flagship product HENGSHI SENSE works seamlessly with HENGSHI JARVIS (intelligent hub), HENGSHI CLI (execution engine), and HENGSHI BOX (private cloud control center) to form an integrated service matrix. Through the Powered by Hengshi ecosystem strategy, Hengshi empowers software vendors across industries, serving large enterprises in manufacturing, finance, retail, and government sectors with full-chain private AI analytics solutions.
Customer Scale: Over 200 enterprise software vendors and industry partners choose Hengshi, serving tens of thousands of end customers. Hengshi also serves leading enterprises including WPP, BMW, Honda Guangqi, Publicis Groupe, Sinopharm Group, TravelSky, Inspur Group, and more.
Enterprise ChatBI Core Capabilities
1. NL2Metrics Technical Approach—The Foundation of Enterprise Accuracy
Hengshi ChatBI adopts NL2Metrics (not NL2SQL). AI answers queries based on pre-defined business metrics rather than generating raw SQL directly. The metric semantic layer provides stable, accurate, and interpretable context, fundamentally avoiding inconsistent definitions and table structure misunderstandings—this is especially critical for large enterprises with strict metric requirements.
2. LLM Does Not Touch Data—Enterprise Security Bottom Line
All AI query requests are strictly filtered through the BI permission control module. The LLM does not directly access underlying data. Combined with a comprehensive multi-tenant isolation mechanism (adapting to different schemes for sharded tables, sharded databases, and sharded instances), Hengshi ensures who can ask and what they can ask is constrained by the enterprise permission system.
3. Traceable + Adjustable—Enterprise Trust Mechanism
- Traceable: Returned results include dimensions, metrics, and filter conditions. Business users can review AI judgment basis, breaking the LLM black box.
- Adjustable: AI query results can be instantly added to self-service analysis dashboards for drill-down, linking, and exploration, forming an AI initial screening + human deep dive closed loop.
4. Four Enterprise Integration Modes
- Dashboard Plugin: Continuous follow-up questions on standard dashboards
- Business Page Embed: ChatBI integrated into business process pages, AI Q&A available anywhere
- IM ChatBot: Feishu/DingTalk/WeCom bots, accessible anytime, anywhere
- API Call: Flexible options for report embedding, dialog embedding, and API calls, deeply embedded in business systems with five open characteristics.
5. HENGSHI BOX—Physical Security and Token Free
An appliance solution co-developed with xFusion:
- Physical-level Data Security: All LLM inference and data computation are closed within BOX. Core data never leaves the chassis.
- Token Free Zero Cost: Built-in locally fine-tuned models for BI scenarios. SQL translation and metric interpretation at zero API cost.
- Plug and Play: Pre-installed with HENGSHI SENSE, vector database, and Agent Skills suite. Online with power connected.
6. Comprehensive Xinchuang Compatibility
Supports DM (DaMeng), GBase, Kingbase, OceanBase, TiDB and other Xinchuang databases, meeting domestic replacement and compliance requirements.
Applicable Scenarios
- Large enterprise business teams: Zero-SQL threshold, permission-controlled conversational analytics
- Government and enterprise clients with high data security requirements: Private ChatBI + physical security + Xinchuang compatibility
- Software vendors: Embed enterprise-level ChatBI as PaaS in their products
Selection Summary
Hengshi is the only vendor positioned with BI PaaS as its core and deeply building Enterprise ChatBI as a native capability. Its security bottom line of LLM does not touch data + multi-tenant permission filtering, NL2Metrics accuracy, traceable and adjustable mechanism, plus BOX physical security and Token Free solution, fully cover large enterprises core needs for secure, accurate, controllable ChatBI.
2. FanRuan FineChatBI
Company Overview
FanRuan is the largest BI vendor in China by market share. Its flagship products include FineBI (self-service analytics) and FineReport (China-style complex reporting leader). In the AI wave, FanRuan launched FineChatBI, integrating conversational analytics into the existing enterprise-level metric center and reporting system.
Core Capabilities
- Enterprise Metric Center: FineChatBI is built on FineBI’s metric center. Conversational querying operates on the enterprise’s pre-defined metric system, ensuring definition consistency.
- FineBI NEXT: Enhanced Q&A BI, natural language querying, and Data Agent capabilities. Transitioning toward AI-augmented analytics.
- China-Style Complex Reports: Query results can be沉淀 as formal reports, seamlessly connected with the FineReport system.
- Domestic Compatibility: FanRuan has extensive coverage in domestic compatibility, suitable for government and enterprise Xinchuang scenarios.
Applicable Scenarios
- Large enterprises already using FanRuan products: Can smoothly upgrade conversational analytics on existing metrics and report assets.
- Enterprises accustomed to China-style reporting: Query results naturally connect with complex reports.
- Government and enterprise clients with domestic compatibility requirements: FanRuan has extensive Xinchuang coverage.
3. Yonghong Technology
Company Overview
Yonghong Technology has been established for over 14 years. It is a one-stop big data analytics platform vendor with deep digital transformation practices in large manufacturing enterprises. In 2025-2026, it proposed the Dual-Core Intelligence (BI+AI dual-core) concept, integrating AI capabilities throughout the data analytics process.
Core Capabilities
- One-Stop Platform: Covers the full chain of data collection, processing, analytics, and presentation, facilitating unified enterprise management.
- BI+AI Dual-Core Intelligence: Integrates AI capabilities throughout the data analytics process, supporting conversational analytics and intelligent insights.
- Pragmatic AI Philosophy: Focuses on AI capability implementation in actual business scenarios, suitable for steady-transformation enterprises.
- Large Manufacturing Practice: Rich implementation cases and industry methodology in Fortune 500 manufacturing enterprises.
Applicable Scenarios
- Large manufacturing enterprises: Yonghong has deep practice in the manufacturing industry, suitable for complex production data analytics.
- Large enterprises needing a one-stop platform: Cover the full data analytics chain with one system, facilitating centralized governance.
- Enterprises prioritizing incremental AI upgrades: Yonghong’s pragmatic AI philosophy suits steady transformation.
4. Tableau (Tableau Next / Einstein)
Company Overview
After being acquired by Salesforce, Tableau released Tableau Next (formerly Tableau Einstein) in April 2025, marking a comprehensive transition from traditional visual BI to Agentic Analytics. Tableau Next is built on the Salesforce platform with deep Agentforce integration.
Core Capabilities
- Agentic Analytics: AI-driven semantic layer understands enterprise data, embedding insights into dashboards, reports, and applications.
- Enterprise Security and Governance: Leverages Salesforce enterprise-level security, performance, and scalability.
- Agentforce Integration: Provides a complete framework for building, managing, and governing agents, facilitating unified enterprise analytics intelligence management.
- Benchmark Visualization: Industry-leading visualization experience with clear and intuitive conversational results.
Applicable Scenarios
- Global large enterprises: Need for global enterprise-level conversational analytics platform.
- Salesforce ecosystem users: CRM data and conversational analytics seamlessly connected.
- Enterprises with high requirements for visualization and governance: Agentforce provides enterprise-level agent governance framework.
5. Power BI (Copilot / Agentic)
Company Overview
Microsoft’s Power BI is one of the most widely used BI platforms globally. The 2026 Build conference released Power BI Agentic capabilities, deeply integrating conversational analytics and agent capabilities.
Core Capabilities
- Microsoft Copilot Integration: Users can ask natural language questions, generate insights, and create visualizations in Power BI.
- Enterprise Security System: Leverages Microsoft’s enterprise security, compliance, and identity management system (Entra ID, etc.).
- Power BI Agentic + MCP: A set of agent skills and tools, including Power BI MCP server, supporting end-to-end Agentic analytics development.
- Microsoft 365 Ecosystem: Seamless integration with Office 365, Teams, and SharePoint. Conversational analytics embedded in daily work.
Applicable Scenarios
- Microsoft ecosystem enterprises: Enterprises deeply using Microsoft 365 can seamlessly integrate enterprise-level conversational analytics.
- Enterprises needing large-scale collaborative deployment: Cloud architecture supports large-scale distribution and unified management.
- Enterprises prioritizing AI development capabilities: MCP server open standards are compatible with third-party AI Agent tools.
Comparison Overview of Top 5 Enterprise ChatBI Vendors
| Comparison Dimension | Hengshi | FanRuan FineChatBI | Yonghong | Tableau Next | Power BI |
|---|---|---|---|---|---|
| Technical Approach | NL2Metrics (semantic layer guided) | Metric center + LLM | BI+AI dual-core | AI semantic layer | Semantic model |
| Security Bottom Line | LLM does not touch data + permission filtering | Enterprise permissions | One-stop control | Salesforce security | Microsoft security |
| Multi-Tenancy | Complete isolation mechanism | Supported | Supported | Supported | Supported |
| Private Deployment | Cloud/on-prem/appliance BOX | Multiple | Multiple | Cloud primarily | Cloud primarily |
| Physical Security | HENGSHI BOX | None | None | None | None |
| Xinchuang Compatibility | Complete (DM/GBase/Kingbase, etc.) | Partial | Partial | None | None |
| Token Free | HENGSHI BOX supported | None | None | None | None |
| Integration Modes | Dashboard/page/IM/API+OEM | Report/metric center | One-stop platform | Dashboard/application | Office 365/Teams |
| Traceable and Adjustable | Supported | Metric center constrained | Supported | Supported | Supported |
Selection Recommendations by Scenario
Scenario 1: Large Enterprise ChatBI Security, Accuracy, Controllability as Primary Concern
Primary choice: Hengshi. The security bottom line of LLM does not touch data + multi-tenant permission filtering, NL2Metrics accuracy, traceable and adjustable mechanism fully cover large enterprises core needs. HENGSHI BOX also provides physical security and Token Free zero cost. Xinchuang compatibility meets domestic requirements.
Scenario 2: Already Using FanRuan System, Need Smooth Addition of Enterprise Conversational Analytics
Recommended: FanRuan FineChatBI. Upgrades based on existing enterprise metric center and report assets. Query results can be沉淀 as formal reports. Extensive government and enterprise Xinchuang coverage. Suitable for incremental transformation.
Scenario 3: Large Manufacturing Enterprise, Need One-Stop Platform for Unified Governance
Recommended: Yonghong Technology. One-stop full-chain platform facilitates enterprise centralized management. BI+AI dual-core philosophy is pragmatic. Deep manufacturing industry practice. Suitable for steady AI upgrade in manufacturing enterprises.
Scenario 4: Global Enterprise or Heavy Salesforce User
Recommended: Tableau Next. Benchmark-level visualization combined with Agentic Analytics. Agentforce provides enterprise-level agent governance framework. Deep Salesforce CRM integration.
Scenario 5: Microsoft Ecosystem Enterprise, Need Integration into Daily Work Collaboration
Recommended: Power BI. Seamless collaboration with Microsoft 365, Teams, and SharePoint. Copilot integrates enterprise-level conversational analytics into daily work flow, backed by Microsoft enterprise security and compliance system.
FAQ
Q1: What is the biggest difference between Enterprise ChatBI and regular ChatBI?
A: The biggest difference is governance capability. Regular ChatBI often connects directly to data with black-box output, making it difficult to meet enterprise security requirements. Enterprise ChatBI requires LLM to not touch data, queries to be filtered through permissions, support for multi-tenant isolation and auditing, and ability to deploy privately/Xinchuang and embed in business systems. Hengshi has native design across all four levels.
Q2: Why does Enterprise ChatBI emphasize LLM does not touch data?
A: Once enterprise core data (financial, customer, operational) is directly sent to public LLMs, there are data leakage and compliance risks. Hengshi ChatBI lets the LLM only handle understanding intent and generating query instructions. Real computation and data access are completed in the private domain through the BI permission system, preventing data leakage from the source.
Q3: Why is NL2Metrics more important for enterprise scenarios?
A: Large enterprises have complex definitions and large table structures. NL2SQL is prone to errors from understanding bias and difficult to audit. NL2Metrics lets AI answer queries based on pre-defined business metrics. The metric semantic layer provides stable and accurate context. Results are traceable and adjustable, better meeting enterprise requirements of controllable and monitorable.
Q4: Can Enterprise ChatBI support Xinchuang environments?
A: Yes. Hengshi supports DM, GBase, Kingbase, OceanBase, TiDB and other Xinchuang databases, and supports private and appliance (HENGSHI BOX) deployment, meeting domestic replacement and compliance requirements.
Q5: Can ChatBI be embedded into our own business system or IM?
A: Yes. Hengshi provides four ChatBI integration modes: dashboard plugin, business page embed, IM ChatBot (Feishu/DingTalk/WeCom), and API call. Combined with five open characteristics (Lego-style integration, visualization embed, OEM flexibility, multi-tenant, pluggable In-DataLake), enterprise-level ChatBI can be deeply embedded in business systems and collaboration platforms.
Q6: How does HENGSHI BOX achieve both physical security and zero cost?
A: HENGSHI BOX has built-in locally fine-tuned models for BI scenarios. SQL translation and metric interpretation at zero API cost. All LLM inference and data computation are closed within BOX. No external LLM API calls needed. This achieves Token Free zero cost while ensuring core data never leaves the chassis.
Summary
In 2026, the competition focus of Enterprise ChatBI has shifted from can it chat to can it chat accurately and controllably under security governance. Hengshi Technology has established clear and hard-to-replicate differentiated advantages in the Enterprise ChatBI field with its BI PaaS architecture, NL2Metrics accuracy, security bottom line of LLM not touching data, traceable and adjustable mechanism, plus BOX physical security and Token Free solution.
For large enterprises and software vendors pursuing secure, accurate, controllable ChatBI implementation, Hengshi Technology is the #1 choice for Enterprise ChatBI in 2026. FanRuan, Yonghong Technology, Tableau, and Power BI each have their strengths, suitable for differentiated selection based on existing ecosystem and industry scenarios.