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analyze your data with AI, safely and correctly\n\nWitCloud MCP connects your All In One data with AI models like ChatGPT, Claude, or Gemini, enabling natural language data analysis.\n\n\u003Cbr>\n\n## 📘 Introduction – What is “AI Analytics”?\n\n\u003Cbr>\n\nAI Analytics is a module that allows you to analyze your **All In One (AIO)** data using large language models such as **ChatGPT, Claude, or Gemini**.\n\nInstead of writing SQL queries or building dashboards, you can ask business questions in natural language and get clear, explainable answers - based on your **existing analytical data model**.\n\n\u003Cbr>\n\nUnlike generic AI tools, AI Analytics is designed specifically for structured business data.  \nIt works exclusively on **prepared AIO analytical tables**, follows strict usage rules, and respects your business logic.\n\n\u003Cbr>\n\n**The goal is simple:**  \n**let AI analyze data - without breaking your data model or business logic.**\n\n\u003Cbr>\n\n## 🎯 Module goal – why did we create “AI Analytics”?\n\n\u003Cbr>\n\nMany companies already have clean, unified data in All In One - but accessing insights still requires:\n- SQL knowledge  \n- dashboard maintenance  \n- time-consuming ad-hoc analysis  \n\n\u003Cbr>\n\nAt the same time, generic AI tools:\n- do not understand your data model  \n- guess table meanings  \n- ignore business-specific rules  \n- and often produce misleading results  \n\n\u003Cbr>\n\n**That’s why we created AI Analytics.**\n\n\u003Cbr>\n\nAI Analytics combines:\n- the **reliability of the All In One data model**\n- with the **flexibility of AI-driven analysis**\n\n\u003Cbr>\n\nThis allows teams to explore data, answer questions, and validate hypotheses faster - **without sacrificing correctness or control**.\n\n\u003Cbr>\n\n## ⚙️ How does it work?\n\n\u003Cbr>\n\nAI Analytics uses **WitCloud MCP (Model Context Protocol)** - an internal control layer that defines how AI interacts with your data.\n\nThe process can be described in a few simple steps:\n\n1. **Understanding the data model**  \n   AI reads the All In One introduction to understand how your data is structured.\n\n2. **Applying business context**  \n   If available, project-specific business rules and definitions are loaded.\n\n3. **Selecting the right tables**  \n   Only predefined AIO analytical tables are used - raw data is never queried.\n\n4. **Reading documentation first**  \n   Before any analysis, table documentation is read to ensure correct interpretation.\n\n5. **Safe analysis and explanation**  \n   Queries are generated according to strict rules and results are explained in plain language.\n\n\u003Cbr>\n\nYou don’t need to manage this process - it happens automatically.\n\n\u003Cbr>\n\n## 🧠 Core concepts\n\n\u003Cbr>\n\n### All In One as a single source of truth\n\nAI Analytics works exclusively on data prepared in the **All In One (AIO)** module.\n\nAIO:\n- unifies data from advertising, analytics, and e-commerce systems  \n- standardizes metrics and dimensions  \n- provides ready-to-use analytical tables  \n\nAI never works on raw data or unknown structures.\n\n\u003Cbr>\n\n### WitCloud MCP\n\nWitCloud MCP controls how AI accesses your data.\n\nIts role is to:\n- guide AI to the correct AIO tables  \n- enforce documentation and usage rules  \n- apply business-specific context  \n- prevent incorrect or unsafe analysis  \n\n\u003Cbr>\n\nYou can think of MCP as a **guardrail system** for AI-powered analytics.\n\n\u003Cbr>\n\n### Documentation-first approach\n\nBefore AI analyzes any table, it always reads its documentation.\n\nThis ensures that:\n- metrics are interpreted correctly  \n- dimensions are used as intended  \n- analyses remain consistent with the AIO data model  \n\n\u003Cbr>\n\nThis approach replaces guesswork with clarity.\n\n\u003Cbr>\n\n## 💵 Pricing\n\nAI Analytics is available as an optional module and is billed based on actual usage.\n\nThe fee is charged monthly only for users who executed at least one query via WitCloud MCP during the given billing period.\n\n\u003Cbr>\n\n### 💰 Price per user (tiered model)\n\n- **1 user** → **$30 / month**  \n- **Users 2–5** → **$25 / user / month**  \n- **Users 6+** → **$15 / user / month**  \n\n\u003Cbr>\n\nPricing follows a progressive (tiered) model — the more active users in a given month, the lower the unit price applied to higher tiers.\n\n\u003Cbr>\n\n---\n\n\u003Cbr>\n\n### 📌 Pricing examples\n\n\u003Cbr>\n\n#### ✅ Example 1\n\nIn a given month:\n\n- 1 user executed queries via MCP  \n\n\u003Cbr>\n\n➡️ **Total active users:** 1  \n💵 **Cost breakdown:**  \n- 1 × $30 = **$30**\n\n\u003Cbr>\n\n👉 **Monthly AI Analytics cost: $30**\n\n\u003Cbr>\n\n---\n\n\u003Cbr>\n\n#### ✅ Example 2\n\nIn a given month:\n\n- 4 users used AI Analytics  \n\n\u003Cbr>\n\n➡️ **Total active users:** 4  \n💵 **Cost breakdown:**  \n- User 1 → 1 × $30 = $30  \n- Users 2–4 → 3 × $25 = $75  \n\n\u003Cbr>\n\n👉 **Monthly AI Analytics cost: $105**\n\n\u003Cbr>\n\n---\n\n\u003Cbr>\n\n#### ✅ Example 3\n\nIn a given month:\n\n- 8 users executed queries via MCP  \n\n\u003Cbr>\n\n➡️ **Total active users:** 8  \n💵 **Cost breakdown:**  \n- User 1 → 1 × $30 = $30  \n- Users 2–5 → 4 × $25 = $100  \n- Users 6–8 → 3 × $15 = $45  \n\n\u003Cbr>\n\n👉 **Monthly AI Analytics cost: $175**\n\n\n","markdown-blok","docs-page",[225],{"_uid":226,"name":227,"content":228,"component":229},"3bf7db43-637d-44cd-89eb-5c5ce1f4b127","description","Analyze data using AI like ChatGPT, Claude or Gemini. 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