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Quadratic

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Listed since

May 20, 2026

Audience

Best for: Data Analysts, Financial Controllers, …

Pricing

Paid

About Quadratic

TL;DR

Quadratic is an AI-powered spreadsheet application designed for data analysts, business intelligence teams, and decision-makers who need to extract actionable insights from their data without complex coding. By seamlessly connecting directly to various data sources, the platform enables users to perform advanced analysis using natural language. Instead of writing intricate formulas or SQL queries manually, users can simply ask questions about their datasets to generate clear, explainable, and visually organized results. The spreadsheet interface remains familiar, making it accessible to those accustomed to traditional grid-based tools while supercharging their workflow with artificial intelligence. Beyond individual analysis, Quadratic facilitates collaboration by allowing teams to easily share their findings and interactive sheets. This tool effectively bridges the gap between raw database information and comprehensible business intelligence, streamlining the process of data exploration and reporting.

Use Cases

Real-world scenarios where Quadratic saves time.

Use Case 1: Live Financial Reporting

Problem: Finance teams spend hours manually exporting CSV files from QuickBooks and updating static spreadsheets to build recurring financial reports.
Solution: Quadratic connects directly to live QuickBooks data, allowing users to pull profit and loss statements, balance sheets, and invoices directly into the spreadsheet and refresh them instantly.
Example: A financial controller builds a dynamic cash flow dashboard that automatically pulls the latest invoice data without requiring manual file exports.

Use Case 2: Multi-Source Data Analysis

Problem: Data analysts struggle to combine live database tables with offline Excel files and PDFs without writing complex SQL joins or VLOOKUP formulas.
Solution: The platform enables users to connect live databases, Excel files, and PDFs in one workspace, allowing them to merge and clean datasets using Python, SQL, or formulas.
Example: An analyst combines customer records from a PostgreSQL database with marketing spend from a local CSV file, using Python to calculate customer acquisition costs.

Use Case 3: AI-Assisted Exploratory Data Analysis

Problem: Business managers need to query large database tables but do not know how to write SQL or Python code to extract insights.
Solution: Users can ask questions in plain English, and the built-in AI automatically generates the underlying Python, SQL, or formulas to execute the analysis, providing clear explanations of the generated code.
Example: A product manager asks "which product segments grew fastest last quarter?" and the spreadsheet writes and executes a Python script to group, calculate, and visualize the trend.