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Hex MagicAI analytics copilot that writes queries, builds charts, and debugs notebooks inside Hex.

5.0 (5)
Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

Hex Magic is an AI analytics copilot integrated within the Hex platform. It assists users in analyzing data, writing queries, building charts, and debugging notebooks. The tool allows users to ask questions in natural language and receive complete analyses with grounded answers based on the organization's context. Hex Magic is designed to work with or without code, making advanced analytics accessible to users with varying levels of technical proficiency. The AI copilot is powered by leading large language models (LLMs) and can understand data and user intent, speeding up analyses and providing a form of 'magical assist' directly within the user's workflow. It can abstract away tedious parts of data work, allowing users to start with any question and get to a first draft in minutes. Hex Magic supports various features such as conversational self-serve analytics, agentic workflows for data exploration, and the creation of data apps that can be shared and interacted with by end-users. The tool also includes governance and security measures, ensuring that metadata is stored securely and that AI session data usage is governed by the organization's terms of service. The Hex Agent can write and edit code, understand dependencies within data flows, and debug work. It can integrate with tools like Slack, Claude, and Cursor, bringing full context to data questions and providing accurate, reliable answers. Overall, Hex Magic aims to empower data teams and non-technical users alike to perform in-depth analyses and make data-driven decisions efficiently.

Key features

  • Natural language to SQL and Python
  • Automated chart generation
  • AI-assisted bug fixing
  • Schema-aware suggestions
  • In-notebook chat interface
  • Workflow automation for analytics tasks

Pricing

Model
Freemium
Rating
5.0 / 5 (5)

Use cases

Natural language to SQL queries

Analysts can ask questions in plain English and have Magic translate them into SQL or Python that runs against connected data sources inside a Hex notebook.

Faster chart and dashboard building

Generate visualizations automatically from prompts or existing cells, accelerating exploratory analysis and dashboard assembly for stakeholders.

Debug broken queries and code

When a SQL query or Python cell errors out, Magic suggests fixes using schema and notebook context, reducing time spent troubleshooting.

Routine data cleanup automation

Use the in-notebook chat to automate repetitive analytics tasks like reshaping, filtering, and standardizing datasets while keeping analyst oversight.

Pros & Cons

Pros

  • Generates SQL and Python from plain English
  • Context-aware of project schema and cells
  • Speeds up chart and dashboard creation
  • Helps debug broken queries and code

Cons

  • Tied to the Hex platform
  • Outputs still need analyst review
  • Quality depends on data model clarity
  • Advanced features may require paid plans

Reviews

5.0

Average from 5 ratings.

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Wei Chen

May 4, 2026

Use it every day

Honestly didn't expect to like it this much. Schema-aware suggestions is exactly what I needed, and helps debug broken queries and code. but I reach for it almost every day now and it just clicks.

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Sanjay Gupta

Dec 2, 2025

Does the job

Pretty happy overall. Natural language to SQL and Python just works and speeds up chart and dashboard creation. Advanced features may require paid plans can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

L

Liam O’Connor

Nov 2, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on workflow automation for analytics tasks, and context-aware of project schema and cells caught me off guard. Advanced features may require paid plans is why this isn't a perfect score, still, I'd recommend giving it a real trial.

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Elena Rossi

Oct 9, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is aI-assisted bug fixing — handled better than most — and generates SQL and Python from plain English. Quality depends on data model clarity is my one real gripe. Worth the time if this is your use case.

P

Priya Nair

Aug 12, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is in-notebook chat interface — handled better than most — and speeds up chart and dashboard creation. Worth the time if this is your use case.

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