See how your business actually runs, not how you think it runs
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AI tools for data analysis
48 tools that do data analysis · best fit first, then monthly traffic · part of 3,707 live tools
AI coding agent for Jupyter notebooks that generates, executes, and explains data science code from natural language descriptions.
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A financial analyst stares at a spreadsheet with 50,000 rows of transaction data
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Sourcetable connects directly to MySQL, PostgreSQL, and MongoDB databases
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A product analyst needs to segment customers by purchase behavior across three years of transaction data
126
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Open Source GenAI powered tool
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You connect Analytify to your database
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Raw data turns into business stories
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A sales manager gets Monday's revenue spreadsheet with 50,000 rows
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Rows is a spreadsheet built around an AI data analyst.
43
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Genspark presents itself as an all-in-one AI workspace where slides, docs, images, video, code and design are produced in a single place.
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GPT for Work brings an AI agent and bulk-processing tools into Excel and Google Sheets.
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Synthetic data that doesn't break privacy rules
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BlueAlpha is an AI-driven tool designed to convert marketing data into actionable insights. The system is designed to help marketing teams maximize the efficacy of campaigns and optimize ad spending.
182
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Connect marketing and business data to AI agents
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ASK BOSCO claims 96% accuracy in forecasting media spend allocation across your marketing channels
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Most urban fleet platforms treat simulation and live operations as separate problems
139
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The Startup plan caps you at two admin roles and 50 embed users
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You can start using Magic Vizion without paying anything upfront
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Connect 43+ business tools to ChatGPT, Claude, and Perplexity. 250+ skills included.
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A retail analytics manager uploads six months of sales data through drag and drop
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Database developers and data analysts need faster ways to spin up environments and query data
152
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This AI actually runs queries on your live Postgres database
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IBM watsonx is a portfolio of enterprise AI products that covers building models and applications, preparing governed data, running AI assistants and agents, and managing AI risk.
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