How to Set Up Rows AI for Spreadsheet Analysis

What Rows AI Does and Why It Matters

Rows is a browser-based spreadsheet that ships with a built-in AI Analyst capable of running calculations, creating columns, generating charts, and cleaning data from natural language prompts. Unlike add-ons or third-party plugins that bolt AI onto an existing spreadsheet, Rows treats AI as a core feature of the grid itself. You type a question, and the platform returns a structured result inside your spreadsheet rather than in a separate chat window.

For analysts, ops managers, and founders who spend hours wrestling with VLOOKUP formulas and pivot tables, Rows replaces that manual work with conversational prompts. According to an independent benchmark run in September 2025, Rows achieved 89% first-try accuracy on analytical tasks, compared to 57% for Google Sheets with Gemini and 53% for Excel Copilot. The tradeoff is speed: Rows takes a median 138 seconds per response versus 10 seconds for Google Sheets, but the accuracy gap means fewer retries and corrections.

Step 1: Create Your Account

Go to rows.com and sign up with Google or email. The free plan includes 20 AI Analyst queries per month, which is enough to test the workflow on a real dataset. No credit card is required. Once logged in, you land on a dashboard showing your spreadsheets and templates.

Step 2: Import Your Data

Rows supports three ways to get data in:

Manual entry or paste works the same as any spreadsheet. Copy a range from Excel or Google Sheets and paste it directly into a Rows table.

CSV and file upload handles standard flat files. Click "Import" in the top menu, select your file, and Rows maps columns automatically. The platform can also extract tables from PDFs using built-in document parsing.

Live data connectors pull directly from over 50 platforms including Google Analytics 4, Google Ads, HubSpot, Stripe, Salesforce, and social media APIs. To connect a source, click "Integrations" in the sidebar, authenticate with your account, and configure which data to pull. You can schedule automatic refreshes so the spreadsheet always has current numbers.

Step 3: Open the AI Analyst

Click the sparkle icon at the bottom right corner of your spreadsheet. This opens the AI Analyst panel on the right side of the screen. The panel works like a chat interface where you type prompts and receive structured results.

The AI Analyst reads your table headers and samples up to five data rows to understand the structure. It does not send your full dataset to an external model. Only headers, sample rows, and basic statistics like min and max values are transmitted, which keeps sensitive data local.

Step 4: Run Your First Analysis

Start with a specific, column-referencing prompt. Instead of typing "tell me about this data," write something like "calculate the average order value grouped by customer segment." The more specific your prompt, the more accurate the result.

Here are practical prompt patterns that work well:

For aggregation: "Sum total revenue by month and show the result as a new table." This creates a summary table below your data with monthly totals.

For column creation: "Add a column that classifies each transaction as High, Medium, or Low based on the amount column, where High is above 1000, Medium is 100 to 1000, and Low is below 100." The AI Analyst adds the column and populates every row.

For cleanup: "Remove duplicate rows based on the email column, keeping the most recent entry by date." This filters your table in place.

For enrichment: "Add a column with the current stock price for each ticker symbol in column B." Rows can pull live financial data directly into cells.

Step 5: Build Charts and Visualizations

Once your data is structured, prompt the AI Analyst to generate charts. A prompt like "create a bar chart showing monthly revenue with months on the x-axis" produces an embedded chart inside the spreadsheet.

For best results, specify the chart type explicitly. Rows supports bar, line, area, pie, scatter, and combo charts. If you leave the chart type unspecified, the AI picks one based on the data shape, which sometimes misses the mark.

Charts in Rows are dynamic. They update automatically when underlying data changes, which matters if you have live connectors feeding fresh numbers into the sheet.

You can also combine multiple chart types into a single view. For example, prompt "create a combo chart with revenue as bars and profit margin as a line, both grouped by quarter" to get a dual-axis visualization. This is useful for comparing absolute values against percentages or rates in one place.

Step 6: Merge and Join Tables

If your analysis spans multiple data sources, the AI Analyst can combine them. Use the @ notation to reference tables by name. For example, "join @Orders and @Customers on the customer_id column and add the customer name and region to the orders table" performs a left join and appends the matched columns.

This works for both tables within the same spreadsheet and across different sheets. The key is specifying the join column explicitly. Ambiguous prompts like "combine these tables" tend to produce incorrect matches, while explicit column references succeed consistently.

Table merging is particularly useful when you pull data from separate live connectors. A marketing team might have ad spend in one table from Google Ads and conversion data in another from HubSpot. Joining them on campaign name or date lets you calculate cost per acquisition directly in the spreadsheet without exporting to a separate tool.

Step 7: Automate with Scheduled Refreshes

If your spreadsheet pulls from a live connector, you can set it to refresh on a schedule. Ask the AI Analyst: "Refresh this Google Ads data every morning at 9 AM." The platform handles the scheduling and updates the connected tables automatically.

This turns a one-time analysis into a persistent, self-updating dashboard. You can embed Rows spreadsheets in Notion, Confluence, or any website using the embed link, giving stakeholders a live view without needing to open the app.

For teams that report weekly or monthly, the combination of live connectors and scheduled refreshes eliminates the Monday morning scramble of pulling fresh CSVs and rebuilding pivot tables. The spreadsheet stays current and the analysis layer on top of it remains intact.

Tips for Getting Accurate Results

Reference column names exactly as they appear in your headers. If your column is named "Rev (USD)," use that exact string in your prompt rather than paraphrasing it as "revenue."

Break multi-step operations into sequential prompts. If you need to group daily data by week and then calculate averages, first prompt "add a column extracting the ISO week number from the date column," then follow up with "calculate the average of the amount column grouped by the new week column."

Use the @ symbol to reference other tables by name when doing cross-table operations like joins or lookups. For example, "join @Orders and @Customers on the customer_id column" tells the AI exactly which tables and keys to use.

Rows creates automatic checkpoints every three prompts. If an operation produces unexpected results, hover over the checkpoint in the sidebar and click the restore icon to revert. Up to 50 checkpoints are stored for seven days.

When Rows Works Best

Rows fits teams that need to analyze operational data regularly but lack dedicated data engineering resources. Marketing teams pulling campaign metrics from multiple ad platforms, finance teams reconciling transaction data from Stripe or QuickBooks, and operations managers tracking inventory across suppliers are common use cases.

The platform handles structured tabular data well. It is less suited for unstructured text analysis, large-scale machine learning, or datasets exceeding a few hundred thousand rows. For quick exploratory analysis on business data that arrives as spreadsheets or API feeds, it removes the formula barrier entirely.

If you prefer to skip the spreadsheet interface altogether and work from raw file uploads with a single prompt, tools like VSLZ handle end-to-end data analysis from upload to charts and statistical output without requiring any grid interaction.

What Comes Next

Once you have a working Rows spreadsheet with live data and AI-driven analysis, the natural next step is sharing it. Use the "Publish" feature to generate a shareable link or embed code. Set view-only permissions for stakeholders who need to see the numbers but should not edit the underlying data. For recurring reports, combine scheduled data refreshes with an embedded dashboard so the output stays current without any manual intervention.