WisdomAI is a platform that helps businesses analyze their data and make informed decisions. It allows users to ask questions in plain English and receive clear, data-driven answers and visualizations, without the need for coding or manual dashboard creation.

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What Does WisdomAI Do?

WisdomAI transforms your company’s raw data into actionable insights through several key capabilities:

Understands Your Questions

You can ask questions in everyday language, like “What was our revenue last quarter?” or “Which products are selling best?” WisdomAI uses advanced Natural Language Processing (NLP) to understand what you’re asking.

Connects to Your Data

It seamlessly connects to various data sources your business uses, such as databases like PostgreSQL and SQL Server, or data warehouses like Snowflake and Google BigQuery.

Organizes Your Business Data

WisdomAI helps you organize your data into domains that represent different parts of your business, like Sales or Marketing. This makes your data more relevant and easier for everyone to use.

Turns Answers into Stories

Instead of just giving you numbers, WisdomAI can create interactive Stories and dashboards with charts and text. These stories update automatically as your data changes, making it easy to share insights across your team.

Automates Workflows

You can set up Playbooks to automate common analytical tasks and standardize how your team analyzes specific business questions.

Learns and Improves

WisdomAI continuously gets smarter based on user feedback. When you provide corrections or refine questions, the system learns to give even better answers in the future.
This means WisdomAI helps you quickly get the information you need from your data, making data-driven decisions simpler and faster for everyone.

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Who Uses WisdomAI?

WisdomAI is built for all kinds of businesses, from small startups to large corporations, across various industries. It’s designed to be useful for different roles within your organization:

Adminis

These are typically IT or BI directors who set up WisdomAI, connect it to your data sources, manage user access, and define the overall structure of your data in the platform.

Explorers

This group includes business users, executives, and other stakeholders who use WisdomAI to ask questions, view reports, run stories, execute playbooks, and export insights for their daily work.
This documentation is devided in a way you can focus on what matters most to your role. For more information on each role, visit the Where to Start section.

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WisdomAI vs Generic RAG Systems

Unlike generic Retrieval-Augmented Generation (RAG) solutions that rely purely on embedding search over documents, WisdomAI builds a structured understanding of your data through semantic modeling, curated context, and validated logic.
FeatureWisdomAIGeneric RAG
Understands data relationships✅❌
Requires semantic modeling✅❌
Validated, testable logic✅❌
Dynamic, auto-updating dashboards✅❌
Tailored for business data✅❌
WisdomAI is purpose-built for enterprise analytics, not just document Q&A.

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Data Flow & Architecture Overview

WisdomAI transforms your business data into actionable insights using a multi-layered pipeline. The following components work together to deliver accurate, contextualized answers and auto-updating data stories.
1

Connected Data Sources

WisdomAI integrates with your data platforms (like Snowflake, BigQuery, or Redshift), acting as the entry point of all information flowing through the system.
2

Semantic Modeling

Admins and data owners enrich the data by documenting tables, columns, and domains. They also define relationships (e.g., one-to-many, many-to-many) to create a business-friendly context for natural language understanding.
3

Natural Language Query (NLQ) Engine

Users ask questions in plain language. The NLQ engine parses these questions and translates them into SQL queries using the underlying semantic model.
4

Answer Generation and Story Builder

Responses are generated and formatted into interactive “Stories”, data-driven narratives that combine visualizations, text, and insights. These can be scheduled and shared.
5

Delivery and Reporting Layer

Stories and answers can be delivered via email or embedded within dashboards, helping teams stay informed with minimal effort.
6

User Feedback and Corrections

Users can give feedback on story accuracy, suggest corrections, or add clarifying context to improve future results.
7

Context Updates and Review Loop

Admins and Editors review user feedback and update the semantic layer accordingly, closing the loop and continuously improving answer quality and domain understanding.

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Next Steps