Quadratic

What is Quadratic?

Quadratic is an AI-powered spreadsheet that lets you ask questions about your data in natural language and generate editable, verifiable insights using spreadsheet-native outputs including Python and SQL. The core purpose is to help people work faster in spreadsheets while keeping their analysis understandable and editable.

Rather than treating results as a black box, Quadratic provides a method of analysis inside the spreadsheet. You can open and edit cells produced by the AI to review and adjust the work as needed.

Key Features

• Natural-language questions for spreadsheet analysis: Ask what you want to see using simple language rather than writing formulas.
• AI-generated analysis you can verify in-place: Quadratic “delivers a method of analysis in your spreadsheet,” with outputs that can be inspected directly.
• Editable AI-written cells: Open and edit any cell created by the AI to check how the result was produced and make changes.
• Advanced analysis beyond standard spreadsheet workflows: The AI can write Python and SQL cells to support more flexible data transformations.
• Collaboration built around verifiable outputs: Teams can share analysis that is based on editable spreadsheet content.
• Templates for starting points: The product includes templates that help you begin analysis tasks.

How to Use Quadratic

1. Open your spreadsheet data in Quadratic and start a new analysis.
2. Ask the AI a question in natural language about what you want to find, compare, or understand in your data.
3. Review the AI’s output inside the spreadsheet and open any generated cells to confirm the logic.
4. Edit the cells as needed (including AI-written Python/SQL cells when applicable) to refine results.
5. Share repeatable insights with others so the analysis can be checked and updated.

Use Cases

• Data QA for missing or inconsistent values: Search large tables for missing fields or inconsistencies and iterate quickly without spending time on manual formula building.
• Cross-query analysis and visualization: Compare multiple queries and build custom charts (the page mentions Plotly) for reporting or exploration.
• Daily workflow for analysts and data scientists: Keep Quadratic as a frequently used “tab” for ongoing cleaning, exploration, and analysis tasks.
• Product management insights from product data: Analyze usage patterns to support better product decisions.
• Marketing and SEO performance review: Evaluate campaign results and optimize SEO work based on the data you bring into the spreadsheet.

FAQ

Does Quadratic require writing spreadsheet formulas?

No. The product is described as supporting questions in simple natural language “no formulas required,” while still allowing you to review and edit the AI’s work in cells.

Can I check and modify what the AI produces?

Yes. Quadratic supports opening and editing any cell produced by the AI so you can verify the analysis and make changes if needed.

What kinds of advanced analysis does Quadratic support?

The page states that Quadratic AI can write Python and SQL cells, enabling analysis beyond typical spreadsheet formulas.

Is Quadratic designed for individuals or teams?

Both. The page describes features for individuals (rapid access to data and answers they can trust) and teams (collaborative analysis with auditable, verifiable outputs).

What security/compliance claims are made on the site?

Quadratic states it is SOC 2 and HIPAA certified and points readers to a trust center for more details.

Alternatives

• BI and dashboard tools: Tools focused on dashboards and reporting can be useful for structured visualization, but may not provide the same editable, spreadsheet-based workflow for inline verification.
• Notebook-style data analysis (e.g., Python/SQL notebooks): Notebooks can handle Python/SQL-heavy workflows, but you may need more effort to keep results easily shared and tied to a spreadsheet-style interface.
• Spreadsheet-only analysis with manual formulas: Standard spreadsheet workflows can be sufficient for formula-driven tasks, but they typically require more manual effort and offer less inline natural-language assistance.
• Low-code analytics platforms: These can streamline analysis and sharing, though they may differ in how directly the underlying logic is editable within the data workspace.