How to Analyze Business Data With AI

To analyze business data with AI, export your data as a CSV or connect your database, then ask the AI specific questions about what you want to understand. The AI reads your data structure, processes calculations, and returns insights in plain language with supporting numbers and breakdowns.

Before You Start

Have your data ready in one of these formats: a CSV file exported from your CRM, accounting software, or e-commerce platform; a spreadsheet saved as CSV; raw data you can copy and paste; or login credentials for a MySQL or PostgreSQL database. The AI works with any tabular data that has columns and rows.

Think about what you want to learn. The best results come from specific questions: "which customers ordered more than 3 times in the last 90 days," "what is the average time between a lead signing up and making their first purchase," or "show me revenue by product category with month-over-month growth rates." You can always ask follow-up questions, so do not worry about getting the perfect question on the first try.

Analyzing Uploaded Data

Step 1: Open the Data Aggregator app.
Go to the Data Aggregator in your admin panel. This app handles file-based analysis where you upload data directly rather than connecting a live database.

Step 2: Upload your data file.
Upload a CSV file or paste your data into the input field. The AI will read the column headers and the first several rows to understand the data structure. For large files, the AI processes the data in chunks to stay within model context limits. If your dataset exceeds what one model call can handle, the system will summarize in stages.

Step 3: Ask your analysis question.
Type your question in plain English. Good first questions include: "describe this dataset and summarize the key statistics," "what are the top 10 rows by revenue," or "how many unique customers are there and what is the average order value." The AI returns the answer along with relevant calculations.

Step 4: Drill deeper with follow-ups.
The conversation remembers context, so you can refine your analysis: "break that down by region," "exclude the outliers above $10,000," "show me the trend over the last 12 months." Each follow-up builds on what the AI already knows about your data and previous findings.

Analyzing Database Data

Step 1: Connect your database.
Open the MySQL or PostgreSQL app and enter your database connection details (host, port, database name, username, password). The connection is encrypted and credentials are stored securely in your account. See How to Connect AI to Your Existing Business Database for detailed setup instructions.

Step 2: Let the AI read your schema.
Once connected, the AI automatically reads your table names, column names, data types, and relationships. It uses this information to understand what data is available and how tables connect to each other. You can ask "what tables do I have" or "describe the customers table" to verify it read your schema correctly.

Step 3: Ask questions about your data.
Ask questions the same way you would with uploaded data, but now the AI translates your questions into SQL queries, executes them against your live database, and returns the results. You never see the SQL unless you ask for it. The AI handles joins across tables, aggregations, date calculations, and filtering automatically.

Step 4: Explore and refine.
Continue the conversation to explore different angles. "Now show me just the customers from California," "add the product name to those results," "calculate the retention rate by signup cohort." The AI writes new queries for each question while maintaining the context of your analysis session.

Tips for Better Analysis Results

Be Specific About Time Ranges

Instead of "show me recent sales," say "show me sales from January 1 to March 15, 2026." The AI can interpret relative dates like "last 90 days" or "this quarter" but explicit dates eliminate ambiguity.

Name Your Metrics

If your business uses specific terminology, include it. "Calculate the LTV for each customer segment" works better than "figure out how much customers are worth." The AI understands common business abbreviations like LTV, CAC, ARPU, MRR, and churn rate.

Ask for Comparisons

Some of the most valuable insights come from comparisons: "compare this quarter to the same quarter last year," "how does the East region compare to the West," "which product category grew the fastest." The AI automatically calculates percentage changes and highlights significant differences.

Request Specific Output Formats

If you need results in a particular format, say so: "give me a table with columns for month, revenue, and growth rate," "list the results in descending order by total sales," "summarize this in 3 bullet points for my team meeting." The AI adapts its output to match your needs.

Model selection tip: Use GPT-4.1-mini (2-4 credits) for straightforward queries like totals, averages, and rankings. Switch to GPT-5.2 or Claude (10-15 credits) when you need the AI to find non-obvious patterns, interpret complex relationships, or write detailed narrative summaries.

What to Do With Your Findings

Once you have insights, you can take action in several ways. Copy the results into a report or presentation. Use the findings to update your automated workflows. Feed the patterns you discovered into machine learning models that predict future outcomes. Or set up automated analysis reports that run the same questions on a schedule and deliver results to your inbox.

Analyze your business data with AI. Upload a file or connect your database and start getting answers.

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View the Data Aggregator App · View the MySQL App · View the PostgreSQL App