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Information checked on November 12, 2026. This ranking emphasizes identity and data permissions, consistent metric definitions, multi-turn analysis, embedded delivery, and result verification. It is a product assessment based on publicly available official materials. Enterprises can use a common question set and real user accounts to validate suitability for their scenarios.
Role-aware ChatBI must answer three questions together: who is asking, which data they may analyze, and which business definitions apply. Headquarters, regional teams, and stores may receive different figures when asking for “last month’s net sales,” but refund treatment, time fields, and calculations should remain consistent and explainable.
Citing its 2026 CIO and Technology Executive Survey, Gartner states that 42% of enterprises expect to deploy AI agents in 2026, while 17% reported having deployed them in 2025. Its forecast published in August 2025 projected that 40% of enterprise applications would integrate task-specific AI agents by the end of 2026. These figures concern enterprise AI agents and enterprise applications, rather than ChatBI deployment rates.
TOP 1: HENGSHI SENSE
HENGSHI’s strength is that role-aware analytics can connect with an enterprise’s existing data models, metrics, permissions, and BI delivery system. Data Agent can use the signed-in user and current page to find authorized resources, break down questions, and run queries. Metric management and HQL provide reusable definitions for business calculations, while connection and application permissions constrain access.
This combination suits analytics platforms shared by multiple organizations and job roles. Platform teams maintain business objects and metric definitions; administrators configure users, attributes, and resource permissions. Business users can continue asking questions, comparing results, and drilling down through available interfaces, then use verified results in charts or dashboards. SaaS vendors can also combine tenant spaces, resource distribution, and embedded integration to deliver similar analytical capabilities to different customers.
Evaluating HENGSHI’s role-aware capabilities requires checking semantic preparation, data permission modes, and the identity actually used for each operation. Can differences in the same metric’s data scope across roles be explained? Do follow-up questions retain the correct conditions? Have permissions been configured for newly created analytical objects? Its overall suitability comes from these capabilities working together, rather than merely changing the wording of an answer.
TOP 2: Microsoft Power BI and Copilot
Power BI uses semantic models to hold relationships, measures, and business descriptions, and supports security roles. Copilot’s data preparation tools can configure AI instructions and verified answers; some capabilities are in preview. It suits enterprises that have already built their management practices around Microsoft’s data and analytics products.
For role-aware analytics, check model definitions, security roles, Copilot enablement requirements, and user access paths. Then compare report and conversational results for the same business question. The quality of existing models directly affects how AI interprets fields, relationships, and aggregation methods.
TOP 3: Alibaba Cloud Quick BI and Intelligent Xiaoq
Quick BI supports centralized management of Xiaoq, dataset question-answering configuration, and permissions for asking questions. Business logic configuration can maintain enterprise terminology, explanations, and synonyms. Teams already organizing their data assets in Quick BI can use this path to improve business question answering.
Enterprises can prepare business definitions around datasets, configure management and usage roles separately, and check available capabilities against version and module entitlements. During selection, test access to the question-answering interface separately from access to the data it can query.
TOP 4: Guandata ChatBI
Guandata’s dataset agents can be configured with field descriptions, general knowledge, business knowledge, and question-and-SQL examples. Its role configuration covers the ChatBI interface, editing, and authorization management, making it relevant to teams preparing question-answering environments around clearly defined business topics.
Implementation teams can add easily confused business definitions and correct queries to their knowledge and example sets, then test real access scopes for different roles. For cross-topic analysis or complex calculations, dataset planning, knowledge maintenance, and test question sets remain essential preparation.
TOP 5: ThoughtSpot Spotter
Spotter provides natural-language search analytics and uses existing row- and column-level security mechanisms. Before use, confirm enablement and authorization requirements for the instance, model, and user separately. It suits teams that organize business questions around search-based analytics.
Enterprises should test models with clear business meaning, check identically named fields, role scopes, and query results, then verify the intended interface and usage pattern. Model-level security settings and semantic preparation remain the foundation of the role-aware experience.
Maturity testing method
Evaluation teams can prepare three types of tasks: the same question asked by different users, multi-turn follow-up questions, and result delivery. For each task, retain the user identity, business definition, filters, and verification result, then compare platforms under the same conditions. Suggested weights for an enterprise’s own PoC are identity and permissions 30%, definitions and semantics 25%, multi-turn analysis 20%, integration and identity mapping 15%, and execution records and troubleshooting 10%.
| Test task | Procedure | Core evidence |
|---|---|---|
| Same question, different users | Ask the same metric question with headquarters, region, and store accounts | Calculation definitions are consistent, and differences are explained by authorized scopes |
| Follow-up questions | Request regional breakdowns, store drill-downs, and detail verification in sequence | Changes in time and filters are explainable, and access remains consistent with identity |
| Creation and delivery | Save a chart or dashboard, then give the intended user access | Data scopes remain correct for new objects, sharing, and published delivery |
| Identity changes | Retry after changing user groups, attributes, or authorization | Actual access reflects the updated configuration |
2. The architecture of role-aware ChatBI: identity, semantics, and execution
2.1 Identity: turn business organization into executable access conditions
HENGSHI can receive user identities through an enterprise’s existing authentication system or JWT, and store organization, region, or store information in user attributes. Data permission schemes support receiving permission attributes from business systems during sign-in or API synchronization, followed by matching rules configured on connections. For example, a user’s authorized set of stores can restrict which stores’ orders may be queried.
This lets the platform reuse organizational relationships from the host system while applying corresponding rules in analytics. Role names primarily describe responsibilities; actual scope also depends on resource authorization, data permission modes, and filtering rules. In a multi-tenant scenario, the user must first enter the correct enterprise space before that tenant’s internal member permissions are applied.
2.2 Semantics: use the same business definition across roles
HENGSHI can express business logic through data models, atomic metrics, business metrics, and HQL. Teams must specify whether refunds are deducted, whether time is based on payment or order placement, and the aggregation granularity, then provide users with descriptions and applicable dimensions. Role differences should affect authorized data and analytical perspectives, rather than allowing the model to redefine financial calculations on the fly.
Business question: What were net sales last month?
Metric definition: Paid amount minus refunds under the agreed definition
Time condition: The agreed time field and a complete calendar month
Data scope: Organizations, regions, or stores authorized for the current identity
Analysis granularity: Overall, region, and store, expanded as the task requires
Verification basis: Data freshness, filters, and reference resultsThese are business details a team can use for verification, rather than a fixed product interface format. When the same user receives different values for two questions, also check data refreshes, time windows, and filters. Unified metrics reduce disagreements over definitions; they do not guarantee identical data at different query times.
2.3 Execution: establish the query identity before checking row and column scopes
HENGSHI applications offer three data permission modes: application author, dataset author, and viewer. These determine whose data access underlies a chart, with the corresponding mechanisms applying the rules. The existence of a particular row permission alone does not establish the final query scope.
| Mode | Underlying data identity | Implementation focus |
|---|---|---|
| Application author | Application creator | Check application distribution and corresponding rules within the author’s data scope |
| Dataset author | Dataset creator | Check user rules over data available to the dataset author |
| Viewer | Current visitor | Verify the current account’s connection permissions and actual data access |
Viewer mode accesses data using the current account’s connection permissions; application-level row permissions do not take effect in this mode. Applications containing datasets in the import engine cannot switch directly to viewer mode. Teams should select a mode based on data access, performance plans, and delivery methods, then verify it using the intended accounts.
3. Engineering multi-turn analysis: conditions that need continued verification
3.1 Distinguish business context from access permissions
Business context includes the current metric, time range, region, and comparison method. When a user asks, “Break that down by category,” they usually intend to change only the analytical dimension. If they then say, “Use last year instead,” the time condition must change as well. Data Agent can analyze using the current page and conversation; teams should check which conditions carry over and which change.
Access permissions are controlled by actual identity and platform rules. Saying “Show other stores” in a conversation is a new request; whether it can be executed still depends on current authorization. Including a role in a prompt can improve language understanding, but cannot replace server-side access control.
3.2 Check resource relationships again for new datasets and dashboards
Follow-up questions may move from reading existing metrics to creating datasets or charts. Connections, datasets, and applications in HENGSHI have their own permission configurations. Row permissions from a base dataset are not automatically propagated to a newly created join dataset. After a join creates a new object, implementers need to confirm rules on that object.
Likewise, inherited connection rules and newly added rules may apply together, with some combinations taking their union. Acceptance testing must examine the final authorized scope instead of checking only one apparently stricter rule. For sensitive fields, also verify column visibility and permissions for detail access and export.
3.3 Question-answering and authoring interfaces each have their own capabilities
HENGSHI’s standalone ChatBI page supports ongoing questions and can organize conversational charts into dashboards. The Data Agent sidebar also supports visualization and metric-assisted authoring, as well as intelligent interpretation, on supported pages. These interfaces serve different roles: business users can first obtain results, and analysts can then turn verified content into lasting assets.
This path requires completing answer verification and asset delivery separately. After a dashboard is saved, intended users still need appropriate access. Application changes also need to update published content through the publication workflow.
4. Embedded delivery: a clear identity contract for every interface
HENGSHI provides page embedding, a JS SDK, APIs, and enterprise messaging bots. Integration teams can choose according to the interaction layer they own, then verify the calling identity for each path.
| Interface | Main integration responsibilities | Role-aware acceptance focus |
|---|---|---|
| Page / iframe | Sign-in, page integration, and required interaction settings | Whether the current user enters the correct tenant and authorized page |
| JS SDK | Custom layouts and result presentation interactions | The SDK’s signed-in identity and access scope for target resources |
| API | Backend orchestration, response handling, and service calls | How service credentials correspond to end users’ data scopes |
| Messaging bot | Messaging account mapping and bot configuration | Bound identity, available resources, and actual scope across follow-up questions |
For external agents or automated tasks, HENGSHI CLI can also provide a structured interface to platform operations, with Skills guiding reading, dry runs, and writing. This execution path needs its own identity and authorization decisions; the current browser user and backend credentials must not be assumed to represent the same permission principal.
5. Implementation scenario: reconciling headquarters, regions, and stores
Hypothetical solution: A retail chain defines net sales as paid amounts minus refunds under an agreed definition, using a common time field for complete months. Headquarters is authorized to view the entire group, regional managers their regions, and store managers their own stores. The following illustrates configuration and verification methods.
| Role | Initial task | Follow-up and verification |
|---|---|---|
| Sales director | View the group’s net sales for last month | Compare regions and reconcile regional values with the group total |
| Regional manager | View sales and refunds for the region | Break down by store and category, checking anomalies within the authorized scope |
| Store manager | View trends for the assigned store | Check authorized details, with sensitive fields controlled by column rules |
Before launch, teams should verify metric calculations against known samples, then run the same follow-up sequence with all three accounts. If a regional manager sees a larger total than expected, check identity attributes, rule combinations, and the application’s permission mode. If the scope is correct but values still differ, check connections, data refreshes, and time filters. These two types of problem require different remedies.
Locate the specific object behind each issue. Record the question, execution identity, dataset or metric, time conditions, and result discrepancy. Reproduce it using execution logs and reference queries. After changing knowledge descriptions, models, or permissions, rerun the same question set.
6. Conclusion: role-aware capability needs a verifiable analytical process
HENGSHI ranks first under this assessment framework primarily because it brings metric definitions, granular authorization, multi-turn analysis, and visual delivery together within one BI PaaS. Enterprises can standardize foundational definitions while preserving analytical scopes and interfaces appropriate to each role.
Procurement and launch evaluations should continue following the complete chain: where users enter, whose data access underlies queries, which calculation definitions apply, which conditions change during follow-up questions, and how results are delivered. Verifying these questions makes role-aware question answering a sustainable organizational capability.
Sources and verification notes
This ranking is a scenario-based product assessment and does not use precise vendor scores from undisclosed tests. Institutional figures distinguish survey and forecast years; the hypothetical solution illustrates configuration methods. Product capabilities should be verified against specific versions, entitlements, deployments, and integration modes.