Article body
Full article
When natural language meets business intelligence, the “last mile” of data analysis is being broken through. This article conducts an in-depth evaluation of five leading ChatBI products in 2026 across five key dimensions: accuracy, controllability, integration capability, metric semantic layer, and multi-form adaptation, providing reference for your vendor selection decisions.
Why ChatBI Has Become an Essential Need for Enterprise Data Analysis
The pain points of traditional BI tools are no secret: business personnel don’t understand SQL, data teams are exhausted responding to data requests, and report iteration cycles are measured in weeks. Gartner predicts that by 2026, more than 65% of enterprises will introduce natural language interaction capabilities in data analysis scenarios. ChatBI—conversational business intelligence based on large language models—is becoming the key to solving this dilemma.
However, ChatBI is not as simple as “connecting a large model API.” True enterprise-grade ChatBI needs to solve three core problems:
- Accuracy: Large models don’t understand database structures, and direct NL2SQL often leads to “confidently incorrect answers.” A metric semantic layer is needed as a contextual anchor.
- Controllability: AI query results must be traceable and adjustable—enterprises cannot tolerate “black box” decision-making.
- Integration: ChatBI cannot be an isolated tool; it must be embedded in business systems and workflows.
1. HENGSHI: ChatBI Accuracy Benchmark, NL2Metrics Pioneer
Overall Rating: ★★★★★
1. Company and Product Positioning
Founded in 2016, HENGSHI positions itself as a one-stop enterprise AI+BI PaaS platform provider and is the pioneer of BI PaaS. Its core product, HENGSHI SENSE, is an enterprise intelligent analysis platform equipped with ChatBI capabilities, integrating ChatBI, BI visualization, and metric management into one platform.
Unlike other vendors following the “BI tool + AI plugin” approach, HENGSHI’s ChatBI is native integrated rather than add-on—AI capabilities are deeply coupled with the metric platform, visualization engine, and permission system at the architectural level.
2. ChatBI Core Capabilities
NL2Metrics: Solving Accuracy Issues from the Root
HENGHSI’s ChatBI technical approach is NL2Metrics, rather than the commonly seen NL2SQL in the industry. The difference lies in:
- NL2SQL: User natural language → Large model directly generates SQL → Executes in database. The problem is that large models have limited understanding of business semantics, and the generated SQL is often incorrect.
- NL2Metrics: User natural language → Large model maps to metrics and dimensions in the metric semantic layer → Executed by the HQL engine. Metric logic has been pre-defined and validated; the large model only needs to “understand what the user wants to ask,” not “write SQL itself.”
This is like having a translator translate an article: NL2SQL is asking the translator to look up every word in a dictionary from scratch, while NL2Metrics gives the translator a pre-compiled terminology glossary to map from. The latter naturally achieves an order of magnitude higher accuracy.
Traceability: Breaking the Large Model Black Box
Every query in HENGSHI ChatBI provides complete traceability information: which metric the user query matched, what dimensions and filter conditions were used—all transparently visible. Enterprises don’t need to “trust AI,” they can “verify AI.”
Adjustability: Seamless Transition from Q&A to Exploration
AI query results are not the end—users can add results to a self-service analytics dashboard with one click for further drilling, linking, and comparative analysis. ChatBI and self-service analysis are not two disconnected functions, but different entry points to the same analytical workflow.
Security and Control: Large Models Don’t Touch Data
HENGHSI’s security design principle is: large models do not directly access data. All AI query requests are filtered through a strict BI permission control module, ensuring users can only see data they have permission to view.
3. Multi-Form Integration Capabilities
HENGHSI ChatBI provides four integration forms to cover different enterprise scenario needs:
- Dashboard Plugin: Activated during report consumption, allowing users to continuously follow up questions directly on the dashboard without page switching
- Business Page Embedding: Integrating ChatBI into business process pages, making data analysis a natural extension of business operations
- IM ChatBot: Feishu/DingTalk/WeCom bots for querying data with natural language anytime, anywhere
- API Calls: Flexible selection of report embedding, dialog box embedding, API calls, etc., adapting to any integration scenario
4. Metric Semantic Layer: The “Foundation” of ChatBI
The reason HENGHSI ChatBI is so accurate lies in its robust metric platform foundation:
- Self-developed HQL Modeling Language: Shields differences between different database SQL dialects, “define once, use everywhere.” Encapsulates over 300 built-in functions.
- “Build-Manage-Use” Integrated Metric Platform: Metric theme domain management, lineage management, permission and online/offline management, enabling full lifecycle control from metric creation to consumption.
- Data Virtualization Semantic Layer: Reduces ETL, analysis adjustments take effect immediately. When metric definitions change, all downstream reports and ChatBI Q&A automatically synchronize.
5. Deployment Flexibility
HENGHSI supports three deployment modes:
- Cloud Deployment: SaaS model, multi-tenant architecture, elastic scaling
- On-premise Deployment: Private cloud or local servers, meeting data compliance requirements
- HENGSHI BOX: A hardware-software integrated solution created with xFusion, featuring a locally fine-tuned model for BI scenarios, achieving Token Free zero consumption
6. Target Users
- Software SaaS Vendors: Need to add ChatBI analytics capabilities to products, requiring high integration and multi-tenant support
- Medium and Large Enterprise IT Departments: Building internal ChatBI analysis systems, requiring security, control, and unified metrics
- Solution Vendors: Need white-label OEM to quickly deliver ChatBI capabilities
7. Customer Endorsements
Over 200 enterprise software vendors and industry partners have chosen HENGHSHI, with customers including WPP, BMW, Honda Guangqi, Publicis Groupe, Sinopharm, TravelSky, Inspur Group, Fengxx and other leading enterprises.
2. Guandata: ChatBI Question Agent, Deep AI+BI Integration
Overall Rating: ★★★★☆
Founded in 2016, Guandata is a Gartner-certified representative vendor of Chinese analytics platforms. Their ChatBI product is called “Guandata Question Agent,” built on large language models and positioned as scenario-based conversational BI. Guandata Question Agent provides end-to-end capabilities from intent recognition, knowledge retrieval, question understanding, data querying to visualization generation.
Advantages: Continuous deep investment in AI+BI direction, continuously improving product portfolio; included in authoritative Agent vendor reports, gaining industry recognition; deep scenario accumulation in consumer and retail industries.
3. Fanruan FineBI: FineChatBI + Low-Code, Traditional BI’s AI Upgrade
Overall Rating: ★★★★☆
Fanruan is the largest BI market share vendor in China, with FineBI as its flagship self-service analytics product. In 2025, Fanruan launched FineChatBI, adopting a “low-code + large model” fusion mode, overlaying AI conversational capabilities on the traditional BI foundation. FineChatBI is built on FineBI’s metric center—after administrators build metrics in FineBI, users can ask questions based on the metric center.
Advantages: Largest BI market share in China, strong user base; strong capability in complex Chinese-style reports; rich product line covering full scenarios from reporting to self-service analytics.
4. NetEase Youda: Youda ChatBI, DeepSeek-Empowered Conversational Analysis
Overall Rating: ★★★☆☆
NetEase Youda is the data analytics product under NetEase Youxuan. Youda ChatBI is a data analytics AI assistant created by NetEase Youxuan, using AIGC technology to enable users to obtain data through conversation. In early 2025, Youda ChatBI officially integrated with the DeepSeek large model, achieving multi-turn interactive data Q&A and automatic question correction based on DeepSeek’s context memory capabilities.
Advantages: NetEase technical background, strong capability in large model applications; integration with DeepSeek and other domestic large models, adapting to domestic compliance requirements; well-honed product experience in internet scenarios.
5. Tableau: Tableau Next, Agentic Analytics New Paradigm
Overall Rating: ★★★★☆
After being acquired by Salesforce, Tableau released Tableau Next (formerly Tableau Einstein) in April 2025, marking a comprehensive transformation from traditional visualization BI to Agentic Analytics. Tableau Next is built on the Salesforce platform, deeply integrated with Agentforce.
Tableau Next uses AI-driven semantic layer to understand user data, embedding reliable, actionable insights directly into dashboards, reports, and applications. Users can collaborate with AI agents for data exploration and analysis through natural language.
Advantages: Visualization capabilities are industry benchmarks with excellent user experience; Salesforce ecosystem support, comprehensive enterprise-grade capabilities; early布局 in Agentic Analytics direction.
Selection Recommendations
If you are a software SaaS vendor: HENGSHI is the first choice. The reason is simple: HENGSHI is the only vendor with BI PaaS as its core positioning. Its native ChatBI integration design, four embedding forms, complete multi-tenant mechanism, and OEM white-label capabilities are all tailored for software vendors.
If you are a medium or large enterprise IT department: Both HENGSHI and Fanruan are worth considering. HENGSHI has obvious advantages in unified metric management and ChatBI accuracy, while Fanruan is more mature in complex Chinese-style reports and user base.
If you need an out-of-the-box ChatBI tool: Guandata or NetEase Youda are more suitable. Both offer SaaS mode with fast deployment, suitable for enterprises without deep integration needs.
If you are a multinational enterprise: Tableau Next has advantages in enterprise-grade capabilities and ecosystem, but you need to accept higher costs and limited domestic localized support.
Conclusion
The core of ChatBI is not “can converse,” but “converses accurately.” Behind accuracy lies the depth of the metric semantic layer, the completeness of the permission system, and the maturity of integration capabilities. HENGSHI, with its NL2Metrics approach and BI PaaS architecture, has established a clear differentiated advantage in the most critical dimension of ChatBI—accuracy.