docs: align documentation language with AI analytics platform narrative (cube-js#10640)

Update positioning language across key docs pages to match the "AI analytics
platform" / "agentic analytics platform" framing used by competitors (Omni,
Hex), while retaining "business intelligence" and "embedded analytics" as
secondary keywords for SEO discoverability.

Key changes:
- Lead with "agentic analytics platform" and "AI analytics platform"
- Add "self-serve", "trusted answers", "conversational analytics",
  "shared context", and "time-to-insight" vocabulary
- Keep "business intelligence" and "embedded analytics" present for search

Made-with: Cursor

Author: keydunov

docs-mintlify/docs/data-modeling/ai-context.mdx

@@ -1,6 +1,6 @@
 ---
 title: AI context
-description: Optimize your data model for AI by using descriptions and the meta ai_context property to provide additional context.
+description: Improve AI accuracy and trust by enriching your semantic layer with descriptions and AI-specific context that helps agents generate better insights.
 ---
 
 When using [Analytics Chat][ref-analytics-chat] or other AI-powered features,

docs-mintlify/docs/data-modeling/overview.mdx

@@ -1,6 +1,6 @@
 ---
 title: Getting started
-description: Introduces how Cube turns warehouse tables into a reusable semantic layer that powers metrics, dimensions, and API-driven analytics without ad hoc SQL per question.
+description: Build a reusable semantic layer that provides the shared context for AI agents, BI dashboards, and embedded analytics — turning warehouse tables into governed metrics and dimensions.
 ---
 
 The data model is used to transform raw data into meaningful business

docs-mintlify/docs/explore-analyze/analytics-chat.mdx

@@ -1,9 +1,9 @@
 ---
 title: Analytics Chat
-description: Overview of the AI chat experience for asking natural-language questions against your semantic layer with optional embedding and Workbook handoff.
+description: Conversational analytics interface for asking plain-language questions and getting trusted, AI-powered insights from your semantic layer.
 ---
 
-Analytics Chat is a conversational interface that allows you to explore your data using natural language. Ask questions and get AI-powered insights without writing queries or building visualizations manually.
+Analytics Chat is Cube's conversational analytics experience — ask questions in plain language and get trusted, AI-powered insights without writing queries or building visualizations.
 
 ## How it works
 

docs-mintlify/docs/explore-analyze/explore.mdx

@@ -1,6 +1,6 @@
 ---
 title: Explore
-description: Start governed, shareable explorations from Analytics Chat or dashboards without creating a workbook, then graduate analysis into Workbooks when needed.
+description: Self-serve data exploration from Analytics Chat, dashboards, or any semantic view — governed by your data model, shareable via URL.
 ---
 
 Explore is a quick way to explore data in your semantic layer either by point and click or with an AI agent. Unlike workbooks, Explore doesn't require you to create a workbook—you can start exploring immediately from dashboard, analytics chat or any semantic view.

docs-mintlify/docs/explore-analyze/workbooks/index.mdx

@@ -1,10 +1,10 @@
 ---
-description: "Build reports with an AI agent, organize analyses across multiple tabs, and share insights with your team."
+description: "Build reports and explore data with AI agents, organize analyses across multiple tabs, and share trusted insights with your team."
 title: Workbooks
 ---
 
-Workbooks allow you to build reports with an AI agent, organize the results of your
-analysis, and share insights with your team.
+Workbooks allow you to build reports and explore data with AI agents, organize the results of your
+analysis, and share trusted insights with your team.
 
 ## Tabs
 

docs-mintlify/docs/getting-started/embed-analytics.mdx

@@ -1,9 +1,9 @@
 ---
 title: Embed analytics
-description: Choose among iframe embeds, the Analytics Chat API, and headless core APIs depending on how much UI and authentication control you need.
+description: Ship agentic embedded analytics in your product — choose among iframe embeds, the conversational Chat API, or headless core APIs for full control.
 ---
 
-Cube offers rich options for embedded analytics. You can embed [dashboards][ref-dashboards] and [analytics chat][ref-analytics-chat] as iframes, use the [Analytics Chat API][ref-chat-api] directly to create your own conversational analytics experience, or use [Core Data APIs][ref-core-apis] directly to build custom visualizations, reporting, and dashboarding experiences.
+Cube offers rich options for embedded analytics. You can embed [dashboards][ref-dashboards] and [analytics chat][ref-analytics-chat] as iframes, use the [Chat API][ref-chat-api] directly to create your own conversational analytics experience, or use [Core Data APIs][ref-core-apis] directly to build custom visualization, reporting, and dashboarding experiences.
 
 ## Embed with iframes
 

docs-mintlify/docs/introduction.mdx

@@ -1,10 +1,10 @@
 ---
 title: Introduction
-description: Cube is the business intelligence platform powered by the open-source semantic layer.
+description: Cube is the agentic analytics platform for business intelligence and embedded analytics, powered by the open-source semantic layer.
 hideTableOfContents: true
 ---
 
-Cube uses AI agents to build data models and enable data consumers to perform analysis. Use AI to quickly build semantic layer and fully control the analytics context.
+The AI analytics platform that combines self-serve conversational analytics, governed data modeling, and embedded analytics — all powered by an open-source semantic layer.
 
 <iframe
   width="100%"
@@ -16,15 +16,15 @@ Cube uses AI agents to build data models and enable data consumers to perform an
   allowFullScreen
 />
 
-Cube is a new generation of a business intelligence and embedded analytics platform built to be used by both humans and AI agents. It empowers different personas across your organization:
+Cube is an AI analytics platform for the whole organization — built to be used by both humans and AI agents. It combines self-serve conversational analytics, governed data modeling, and embedded analytics experiences, all on top of an open-source semantic layer. It empowers different personas across your organization:
 
-- **Data Engineers** can quickly curate data models with AI assistance, accelerating the development and maintenance of semantic layers
-- **Data Analysts** can perform deep analysis with AI assistance, diving into complex data relationships and patterns
-- **Business Users** benefit from workbooks and dashboards that Cube can automatically build and maintain
+- **Data Engineers** can quickly curate data models with AI assistance, accelerating development and reducing time-to-insight for the whole organization
+- **Data Analysts** can perform deep analysis with AI assistance, getting trusted answers without writing ad-hoc SQL
+- **Business Users** can self-serve with natural language questions, workbooks, and dashboards — no tickets to the data team required
 
 ## How is Cube different?
 
-At the foundation of Cube's agentic analytics platform is an [open-source semantic layer](https://github.com/cube-js/cube)—the critical infrastructure that enables both AI agents and humans to work with trusted, consistent data.
+At the foundation of Cube's agentic analytics platform is an [open-source semantic layer](https://github.com/cube-js/cube) — the shared context that enables both AI agents and humans to work with trusted, consistent data.
 
 The semantic layer provides the governed data foundation that makes agentic analytics possible. It organizes data from your cloud data warehouses into centralized, consistent definitions that AI agents can reliably query, explore, and reason about. Without a semantic layer, AI agents would struggle with inconsistent metrics, scattered business logic, and ungoverned data access—making their outputs unreliable and potentially dangerous.
 

docs-mintlify/embedding/index.mdx

@@ -1,9 +1,9 @@
 ---
-description: Embed Cube analytics into your applications using iframes, the React SDK, or API-first approaches.
+description: Embed AI-powered analytics into your applications using iframes, the React SDK, or API-first approaches.
 title: Overview
 ---
 
-Cube provides multiple approaches for embedding analytics into your applications. Choose the method that best fits your use case and technical requirements.
+Cube provides multiple approaches for embedding AI-powered analytics into your applications — from iframe embeds to fully custom embedded analytics experiences built on Cube's APIs.
 
 ## Iframe-based embedding
 
@@ -33,7 +33,7 @@ Build fully custom embedded analytics experiences using Cube APIs directly. This
 
 ### Chat API
 
-Use the [Chat API](/reference/embed-apis/chat-api) to build custom conversational analytics experiences. This API-first approach lets you programmatically integrate AI-powered conversations with your data, giving you full control over the chat interface and user experience.
+Use the [Chat API](/reference/embed-apis/chat-api) to build custom conversational analytics experiences. Let your users ask questions in plain language and get trusted answers powered by your semantic layer — without building your own AI infrastructure.
 
 ### Core Data APIs