The AI Data Analyst for Governed Conversational Analytics
Ask business questions. Get trusted answers. Actian AI Analyst delivers conversational analytics built on deterministic semantics, enabling business users to explore data confidently while maintaining enterprise governance.
Businesses today are data-rich, but insights-poor
Wasted Time
Data teams waste time with repetitive requests.
Source: McKinsey
Wasted Data
Business data goes unused for decision-making.
Source: Forrester
Wasted Dashboards
Dashboards go unused after 90 days.
Source: Gartner
Answer business questions. Make trusted decisions.
AI Analyst delivers conversational analytics powered by a governed semantic layer and controlled analytical execution.
Model your business
Structure governed metrics and relationships before analysis begins
AI Analyst runs on a semantic layer that reflects how your business actually operates. Models, metrics, glossary terms, and relationships are generated and maintained with the Steward Agent, ensuring business logic is defined before questions are asked.
Ask anything
Query governed data in plain language
Business users engage AI Analyst through conversational analytics to explore governed data without writing SQL. Context is preserved across interactions, enabling deeper analysis while remaining grounded in defined business logic.
Trust the answer
Get accurate results grounded in defined business logic
Every query runs on a semantic layer generated and maintained with the Steward Agent. Steward continuously monitors semantic layer health and surfaces reviewable action plans to keep analytics accurate and reliable over time.
AI engineered for accuracy
Actian AI Analyst enables governed conversational analytics by offering:
Conversational AI agents
Business users ask questions naturally and receive structured analytical answers without writing SQL. Context is preserved across follow-up questions to support deeper exploration.
Steward AI agent-built semantic layer
The Steward Agent generates and maintains your semantic layer by structuring models, metrics, glossary terms, and relationships from existing data assets. It continuously monitors definitions and data connections, surfacing reviewable action plans to ensure queries run on defined business logic.
Controlled analytical execution
Defined relationships and governed metrics constrain how queries are generated and translated into SQL. Analytical paths are structured and consistent, reducing ambiguity and preventing metric drift.
Transparent and scoped access
Every answer includes visibility into joins, filters, and metric calculations. Agent access can be scoped to specific models, dimensions, and measures to protect sensitive data and maintain clarity.
Context-aware conversational analytics in action
Enable leaders to ask performance questions and receive trusted, metric-aligned answers instantly. Follow up within the same thread to explore trends and drivers without waiting on new reports.
Create multi-step research plans that analyze data across models and metrics, then generate synthesized, executive-ready reports. Deep Analysis supports complex investigations that move beyond single-question responses.
Deliver conversational analytics directly inside collaboration tools where decisions are made. Context is preserved within threads so teams explore data collectively and stay aligned on shared metrics.
Expand access to trusted analytics while maintaining centralized control over models, metrics, and sensitive data. Scoped access ensures users explore only approved dimensions and measures, reducing reporting drift.
Structured intelligence,
not guesswork
Launch quickly, keep metrics aligned, and maintain control as usage scales.
Define models, metrics, and relationships in one shared layer so every answer follows consistent business logic. Explicit joins reduce ambiguity across departments.
The Steward Agent generates and validates the semantic foundation from existing tables and documentation. Move from connection to production-ready analytics in hours or days.
See how every answer is built, including joins, filters, and metric calculations. Scope agent access down to specific models, dimensions, and measures.
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Define models, metrics, and relationships in one shared layer so every answer follows consistent business logic. Explicit joins reduce ambiguity across departments.
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The Steward Agent generates and validates the semantic foundation from existing tables and documentation. Move from connection to production-ready analytics in hours or days.
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![]()
See how every answer is built, including joins, filters, and metric calculations. Scope agent access down to specific models, dimensions, and measures.
Built for warehouse-native analytics
Actian AI Analyst operates on curated, modeled data in your data warehouse. Data from ERP, CRM systems, marketing platforms, and other operational tools first flow through ETL/ELT pipelines before enabling conversational analytics.
Built for controlled enterprise analytics
Defined analytical paths
Relationships between models are explicitly defined within the semantic layer, ensuring conversational queries follow controlled a
Scoped access controls
Users and agents are limited to approved models, dimensions, and measures. Conversational access operates within existing governan
Full execution visibility
Every answer exposes joins, filters, and metric calculations so results can be validated and trusted across teams.
Defined logic behind every answer
Shared business definitions
Centrally defined metrics, dimensions, and glossary terms ensure consistent results across teams and conversations.
Steward-built semantic layer
The Steward Agent generates and maintains models and relationships so queries execute against structured business logic.
Controlled conversational execution
Defined relationships constrain joins and guide query generation to reduce ambiguity across datasets.
Scoped and transparent access
User and agent access is limited to approved models and fields, with visible joins and metric calculations.
A controlled path from question to decision
Conversational questions
Users ask questions in conversational language through Slack, Teams, or the Actian AI Analyst app. Follow-up questions retain context, allowing analysis to evolve naturally.
Use for: KPI checks, business exploration, cross-functional analysis.
Steward-built semantic foundation
The Steward Agent builds and maintains a semantic layer of models, metrics, and relationships. Queries execute against defined business logic rather than raw database schema.
Use for: Consistent definitions, governed metrics, reduced modeling effort.
Governed execution and delivery
Conversational queries follow structured analytical paths and execute in your warehouse. Results expose joins, filters, and metric calculations within collaborative workflows.
Use for: Accurate reporting, transparent validation, faster decisions.
Try AI Analyst in Your Environment
- Ask business questions naturally and see governed answers instantly.
- Experience how the Steward Agent builds a semantic foundation from your data.
- Validate results with visible joins, filters, and metric calculations.
- Explore collaborative analytics inside Slack and Teams using trusted definitions.
Ask business questions naturally and see governed answers instantly.Book a Live Demo of AI Analyst
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