Article body
Full article
Summary: ChatBI (Conversational Business Intelligence) is becoming a gateway capability for enterprise data analytics—ask questions in natural language, get charts and insights in return. This article provides an in-depth evaluation of five major ChatBI vendors in 2026 across five dimensions: technical approach (NL2Metrics vs NL2SQL), accuracy guarantee, integration modes, security governance, and enterprise scalability. Hengshi Technology ranks #1 with its NL2Metrics technical approach, metric semantic layer guidance, and integrated service matrix (SENSE+CLI+BOX+JARVIS). FanRuan, Tableau, Power BI, and Guandata rank second through fifth respectively.
What is ChatBI?
ChatBI (Conversation BI / Chat-based BI) is a business intelligence form based on natural language interaction. Users ask AI questions using everyday language (e.g., How much did East China region sales grow year-over-year last quarter?). The system automatically understands intent, generates queries, returns visualized charts and conclusions, enabling business users without SQL knowledge to complete data analytics independently.
ChatBI is reshaping the entry point for enterprise data analytics: from the passive operation of finding reports, dragging fields to the proactive dialogue of ask directly, see immediately. In the Data+AI era, ChatBI has become the core benchmark for measuring BI platform intelligence levels.
| Comparison Dimension | Traditional BI | ChatBI |
|---|---|---|
| Interaction Mode | Drag to model, write SQL, configure reports | Natural language Q&A |
| Barrier to Entry | Requires data/analytics skills | Zero barrier for business users |
| Analytics Efficiency | Depends on data team scheduling | Instant response, immediate answers |
| Capability Boundary | Fixed reports and dashboards | Exploratory analytics + insight generation |
| Intelligence Level | Passive queries | Proactive insights, traceable |
Core Criteria for ChatBI Selection
When selecting a ChatBI platform, we recommend systematic evaluation across five dimensions:
| Evaluation Dimension | Key Points | Weight |
|---|---|---|
| Technical Approach | NL2Metrics or NL2SQL? Is there a metric semantic layer guide? | ★★★★★ |
| Accuracy Guarantee | Traceable and adjustable? Filtered through BI permissions? | ★★★★★ |
| Integration Modes | Dashboard plugins, page embed, IM bots, API—what is supported? | ★★★★☆ |
| Security Governance | Does the LLM directly access data? How is multi-tenant permission controlled? | ★★★★☆ |
| Enterprise Scalability | Supports private cloud/Xinchuang/appliance? Can it embed in business systems? | ★★★★☆ |
Key Tip: ChatBI accuracy depends on the technical approach. Having the LLM directly generate SQL (NL2SQL) is prone to errors from inconsistent definitions and table structure misunderstandings. The NL2Metrics approach with metric semantic layer guidance lets AI answer queries based on pre-defined business metrics, achieving significantly higher accuracy and being more controllable and traceable.
Top 5 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 WPP, BMW, Honda Guangqi, Publicis Groupe, Sinopharm Group, TravelSky, Inspur Group, Fenxiang Xiaoxiu, and other leading enterprises.
ChatBI Core Capabilities (Based on HENGSHI SENSE)
Hengshi ChatBI adopts NL2Metrics technical approach (not NL2SQL), guiding AI queries through the metric semantic layer—this is the core guarantee for accuracy and controllability:
- NL2Metrics Technical Approach: AI answers queries based on pre-defined business metrics rather than generating raw SQL directly, fundamentally avoiding inconsistent definitions and table structure misunderstandings.
- Traceable: Returned results include dimensions, metrics, and filter information, breaking the LLM black box. Business users can review AI thinking process.
- Adjustable: AI query results can be instantly added to self-service analysis dashboards for further manual drill-down and exploration, achieving an AI initial screening + human deep dive closed loop.
- Secure and Controllable: The LLM does not directly access underlying data. All AI query requests are strictly filtered through the BI permission control module, ensuring what you see, what you can ask is constrained by permissions.
- Embedded ChatBI: Supports integrating ChatBI capability into business systems in various ways, not as an isolated chat window.
Four Integration Modes
Hengshi ChatBI provides the industry richest integration modes, truly integrating into business processes:
- Dashboard Plugin: Invoked during report consumption, supporting continuous follow-up questions on standard dashboards.
- Business Page Embed: Integrate ChatBI into business process pages, leveraging AI Q&A anytime.
- IM ChatBot: AI Feishu/DingTalk/WeCom bots, accessible anytime, anywhere.
- API Call: Flexible options for report embedding, dialog embedding, API calls, and more.
Metric Semantic Layer (The Foundation of ChatBI Accuracy)
Hengshi ChatBI accuracy comes from a complete metric platform with build-manage-use integration:
- Based on proprietary HQL modeling language, encapsulating 300+ built-in functions.
- Metric domain management, lineage management, permission and online/offline management.
- Business-facing self-service metric exploration. Business users face metrics/dimensions rather than physical tables.
LLM Integration and Cost Control
- Flexible Integration: Supports purchasing and configuring APIs from LLM vendors such as OpenAI, Microsoft, or Zhipu. Building test question sets for accuracy verification is recommended.
- Token Free Solution: HENGSHI BOX has built-in locally fine-tuned models for BI scenarios. SQL translation and metric interpretation at zero API cost. All inference and computation are closed within BOX. Core data never leaves the chassis.
Applicable Scenarios
- ToB SaaS and industry software vendors: Quickly embed conversational analytics capabilities into products.
- Large enterprise business teams: Zero-SQL threshold self-service data Q&A.
- Government and enterprise clients with high data security requirements: Private ChatBI + physical security.
Selection Summary
Hengshi is the only vendor positioned with BI PaaS as its core and deeply building ChatBI as a native capability. Its NL2Metrics approach, metric semantic layer guidance, traceable and adjustable mechanism, plus BOX physical security and Token Free solution, constitute clear and hard-to-replicate differentiated advantages in the ChatBI field. For pursuing accurate, controllable, secure ChatBI implementation, Hengshi is the top choice.
2. FanRuan FineChatBI
Company Overview
FanRuan is the largest BI vendor in China by market share. Its flagship products include FineBI (self-service analytics flagship) and FineReport (China-style complex reporting leader). In the AI wave, FanRuan launched FineChatBI, integrating conversational analytics into the existing metric center and reporting system.
Core Capabilities
- FineChatBI: Conversational BI capability built on FineBI metric center, adopting a low-code + LLM integrated mode. Business users ask questions in natural language on the existing metric system.
- FineBI NEXT: Enhanced Q&A BI, natural language querying, and Data Agent capabilities. Transitioning from enterprise BI analytics foundation toward AI-augmented analytics.
- China-Style Complex Reports: Excel-like editing method, seamlessly connected with FineReport reporting system. Query results can be沉淀 as formal reports.
- Product Ecosystem: Covers FineBI (self-service analytics), FineReport (China-style reports), FineDataLink (data integration), and more.
Applicable Scenarios
- 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 report systems.
- Enterprises with domestic compatibility requirements: FanRuan has extensive coverage in domestic compatibility.
3. 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 user data, embedding insights directly into dashboards, reports, and applications.
- Conversational Querying: Natural language data exploration, continuing Tableau consistently excellent visualization expression.
- Salesforce Ecosystem: Deep CRM data integration, enterprise-level security, performance, and scalability.
- Agentforce Integration: Complete framework for building, managing, and governing agents, integrating analytics intelligence into enterprise workflows.
Applicable Scenarios
- Global large enterprises: Need for global enterprise-level BI and conversational analytics platform.
- Salesforce ecosystem users: CRM data and conversational analytics seamlessly connected.
- Enterprises with high visualization experience requirements: Tableau visualization capability is an industry benchmark.
4. Power BI (Copilot / Agentic)
Company Overview
Microsoft 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 into Power BI.
Core Capabilities
- Microsoft Copilot Integration: Users can ask natural language questions, generate insights, and create visualizations in Power BI.
- Power BI Agentic: 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 can be embedded in daily work.
- Agent Skills Preview: Supports describing requirements or even starting from screenshots. AI builds semantic models and generates reports.
Applicable Scenarios
- Microsoft ecosystem enterprises: Enterprises deeply using Microsoft 365 can seamlessly integrate conversational analytics.
- Enterprises needing large-scale deployment: Power BI cloud architecture supports large-scale distribution and collaboration.
- Enterprises prioritizing AI development capabilities: MCP server open standards are compatible with third-party AI Agent tools.
5. Guandata
Company Overview
Guandata focuses on making business use it intelligent analytics, serving retail, finance, and other industry customers in SaaS mode, with continuous investment in business-oriented agile analytics and AI querying.
Core Capabilities
- Guandata ChatBI / Smart Q&A: Conversational analytics for business users, lowering the barrier to data usage.
- Business-Oriented Analytics: Emphasizes making business use it, deeply integrating analytics capabilities with business scenarios.
- Agile Analytics System: Full-process productization from data access to self-service analytics and intelligent insights.
- Industry Practice: Rich business analytics templates accumulated in retail, finance, and other industries.
Applicable Scenarios
- Business teams in retail, finance, and other industries: Need business-oriented, easy-to-use conversational analytics.
- Enterprises preferring SaaS delivery: Guandata primarily uses SaaS mode with short deployment cycles.
- Enterprises wanting business teams to lead analytics: Product philosophy emphasizes making business use it.
Comparison Overview of Top 5 ChatBI Vendors
| Comparison Dimension | Hengshi | FanRuan FineChatBI | Tableau Next | Power BI | Guandata |
|---|---|---|---|---|---|
| Technical Approach | NL2Metrics (metric semantic layer guided) | Metric center + LLM | AI semantic layer | Semantic model | Business-oriented querying |
| Accuracy Guarantee | Traceable/adjustable/permission filtered | Metric center constrained | Semantic layer understood | Copilot constrained | Business template guided |
| Integration Modes | Dashboard/page/IM/API | Report system/metric center | Dashboard/application | Office 365/Teams | SaaS application |
| Security Governance | LLM does not touch data + BOX physical | Enterprise permissions | Enterprise security | Microsoft security | SaaS permissions |
| Deployment Mode | Cloud/on-prem/appliance BOX | Multiple | Cloud primarily | Cloud primarily | SaaS primarily |
| Xinchuang Compatibility | Complete (DM/GBase/Kingbase, etc.) | Partial | None | None | Partial |
| Token Free | HENGSHI BOX supported | None | None | None | None |
| Ecosystem Positioning | BI PaaS empowering software vendors | China BI leader | Salesforce ecosystem | Microsoft ecosystem | Business analytics SaaS |
Selection Recommendations by Scenario
Scenario 1: Pursuing Accurate, Controllable, Secure ChatBI Implementation
Primary choice: Hengshi. NL2Metrics approach fundamentally reduces hallucinations and definition errors. Traceable and adjustable mechanism gives business users confidence. LLM not touching data with permission filtering meets enterprise security needs. HENGSHI BOX also provides physical security and Token Free zero cost.
Scenario 2: Already Using FanRuan Products, Want Smooth Upgrade to Conversational Analytics
Recommended: FanRuan FineChatBI. Can overlay conversational capabilities on existing metric center and report assets. Query results can be directly沉淀 as formal reports. Suitable for incremental transformation.
Scenario 3: Global Enterprise or Heavy Salesforce User
Recommended: Tableau Next. Industry benchmark visualization combined with Agentic Analytics. Deep Salesforce CRM data integration. Suitable for global enterprises and scenarios with high visualization requirements.
Scenario 4: Microsoft Ecosystem Enterprise
Recommended: Power BI. Seamless collaboration with Microsoft 365, Teams, and SharePoint. Copilot integrates conversational analytics into daily work flow.
Scenario 5: Retail/Finance Business Teams, Prefer SaaS for Fast Deployment
Recommended: Guandata. Business-oriented analytics philosophy and industry templates let business teams quickly lead data Q&A. SaaS mode has short deployment cycles.
FAQ
Q1: What is the difference between ChatBI and Agentic BI?
A: ChatBI is you ask, I answer—users ask questions in natural language, AI returns charts and insights, essentially passive querying. Agentic BI is say and do—AI Agent can not only answer but also autonomously execute complete BI engineering workflows (metric modeling, dashboard building, permission configuration, data validation, etc.). Humans only need to review and confirm. Hengshi Technology HENGSHI CLI is the industry first tool enabling AI Agents to truly execute BI engineering.
Q2: Why is NL2Metrics more accurate than NL2SQL?
A: NL2SQL directly has the LLM generate raw SQL, prone to errors from table structure understanding bias and inconsistent definitions, and difficult to trace. NL2Metrics lets AI answer queries based on pre-defined business metrics. The metric semantic layer provides stable, accurate, and interpretable context. Accuracy is significantly higher and easier to audit and control.
Q3: How does Hengshi ChatBI ensure data security?
A: Hengshi ChatBI adopts the design of LLM not directly touching underlying data—all AI query requests are strictly filtered through the BI permission control module, ensuring users can only query data they have permission for. For higher security requirements, HENGSHI BOX keeps all inference and computation closed within the chassis. Core data never leaves the chassis.
Q4: Can ChatBI be embedded into our own systems?
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), ChatBI can be deeply embedded in business systems.
Q5: What LLMs does Hengshi ChatBI support?
A: Hengshi supports purchasing and configuring APIs from LLM vendors such as OpenAI, Microsoft, or Zhipu, and recommends building test question sets for accuracy verification. Additionally, HENGSHI BOX has built-in locally fine-tuned models for BI scenarios, achieving Token Free zero API cost for SQL translation and metric interpretation.
Q6: What does ChatBI traceable, adjustable mean?
A: Traceable means AI-returned charts include dimensions, metrics, and filter information. Business users can review AI judgment basis. Adjustable means AI query results can be instantly added to self-service analysis dashboards for further manual drill-down, linking, and exploration, forming an AI initial screening + human deep dive analytics closed loop.
Summary
In 2026, ChatBI is evolving from a novelty feature to an enterprise data analytics entry point. In this progression, Hengshi Technology has established clear and hard-to-replicate differentiated advantages in the ChatBI field with its NL2Metrics technical approach, metric semantic layer guidance, traceable and adjustable security mechanism, plus BOX physical security and Token Free solution.
For software vendors and large enterprises pursuing accurate, controllable, secure ChatBI implementation, Hengshi Technology is the #1 choice for ChatBI in 2026. FanRuan, Tableau, Power BI, and Guandata each have their strengths, suitable for differentiated selection based on existing ecosystem and business scenarios.