How to Use Rows AI for Spreadsheet Analysis

What Rows AI does and why it matters

Rows is a spreadsheet tool that runs entirely in your browser and ships with an AI analyst built into every sheet. Instead of learning formula syntax or writing Python scripts, you type questions in plain English and the platform returns tables, charts, and calculated columns directly in the spreadsheet grid.

This matters for anyone who spends hours each week exporting CSVs from business tools, cleaning data in Excel, and manually building pivot tables. Rows replaces that loop with live data connections and natural language prompts that handle the same work in seconds.

The platform connects to over 50 data sources natively, including Google Analytics, Stripe, HubSpot, Salesforce, and social media platforms. Once connected, data refreshes on a schedule you set, which means your analysis stays current without repeated manual exports.

Getting started with Rows

Setting up takes about two minutes. Go to rows.com and sign up with a Google account or email address. No desktop installation is needed since everything runs in the browser.

Once logged in, you land on a dashboard that looks similar to Google Sheets. You can start with a blank spreadsheet or choose from templates covering sales tracking, marketing dashboards, and financial models.

To import data, click the "+" icon and select your source. For a CSV or Excel file, drag it into the sheet. For a live connection, pick the integration (for example, Google Ads) and authenticate with your account credentials. Rows will pull the data into a new table automatically.

Using the AI Analyst

The AI Analyst is the core feature that separates Rows from traditional spreadsheets. You access it by clicking the sparkle icon in the bottom-right corner of any sheet. A panel opens on the right side where you type your questions.

Here is a practical walkthrough using a sample sales dataset with columns for Date, Product, Region, Revenue, and Units Sold.

Step 1: Get a summary. Type: "What is the total revenue and units sold by product category?" The analyst scans your table headers and a sample of the data, then creates a summary table with the aggregated numbers.

Step 2: Add a calculated column. Type: "Add a column showing revenue per unit for each row." Rows inserts a new column with the formula applied to every row automatically.

Step 3: Build a chart. Type: "Create a bar chart showing total revenue by region." A chart appears directly in the spreadsheet, pulling from the data in your table.

Step 4: Spot anomalies. Type: "Highlight the top 5 days by revenue and flag any days where units sold dropped below 10." The analyst applies conditional formatting and flags the relevant rows.

Step 5: Cross-table analysis. If you have a second table (for example, marketing spend by region), reference it with the @ symbol: "Compare @MarketingSpend with @SalesData and show ROI by region." The analyst joins the tables and produces a merged output.

Working with AI functions

Beyond the analyst panel, Rows offers 14 built-in AI functions that work like regular spreadsheet formulas. These are useful for batch operations across hundreds or thousands of rows.

Some of the most practical functions include CLASSIFY_AI, which sorts text into categories you define. For example, =CLASSIFY_AI(A2, "Engineering, Billing, Sales") will categorize support tickets based on their content. SENTIMENT_ANALYSIS_AI evaluates the emotional tone of text in a cell, which is useful for processing customer reviews or survey responses. EXTRACT_AI pulls specific data points from unstructured text, like extracting zip codes or company names from a block of text with =EXTRACT_AI("Zip code", A2). SUMMARIZE_AI condenses long text into a format you specify, such as =SUMMARIZE_AI(A2, "two bullet points").

These functions process data row by row, so you write the formula once and drag it down the column. Rows handles the API calls to its language model backend transparently.

Connecting live data sources

One of the strongest use cases for Rows is replacing manual CSV export routines. Instead of downloading a report from Google Ads every Monday morning, you connect the integration once and set a refresh schedule.

To set this up, open a new table, click "Data Source," and select your platform. After authenticating, choose which metrics and dimensions to pull. Rows creates the table with live data. You can then tell the AI Analyst: "Refresh this data every weekday at 9 AM." The table updates automatically, and any analysis or charts built on top of it update as well.

Supported integrations span advertising (Google Ads, Facebook Ads, LinkedIn Ads), analytics (Google Analytics, Mixpanel), CRM (HubSpot, Salesforce), payments (Stripe), and social media (Instagram, YouTube, TikTok). For sources without a native connector, you can use the HTTP integration to pull from any REST API.

Practical tips for better results

Getting useful output from the AI Analyst depends on how you frame your prompts. Based on testing, a few patterns consistently produce better results.

First, reference column names explicitly. Instead of "show me trends," write "show the weekly trend for the Revenue column grouped by Product." The analyst uses your column headers to understand context, so matching them exactly removes ambiguity.

Second, break complex requests into steps. If you need a calculated column and then a chart based on that column, ask for the column first, verify it looks correct, then ask for the chart. Chaining too many operations in one prompt can produce incomplete results.

Third, use the @ symbol to reference specific tables when your spreadsheet has multiple tabs. This prevents the analyst from guessing which dataset you mean.

Fourth, for data cleaning tasks, be specific about what "clean" means. Instead of "clean this data," say "remove rows where the Revenue column is empty and trim whitespace from the Product column." The more precise your instruction, the more reliable the output.

Limitations to know about

Rows AI works best for structured tabular data and mid-size datasets. For very large files (over 100,000 rows), performance slows noticeably. The AI Analyst processes requests using table headers and a sample of up to five rows, which means it may miss patterns that only appear deeper in the dataset.

The platform does not support offline use since it runs entirely in the browser. Formulas and AI functions require an active internet connection. On the free plan, AI queries are limited to a monthly cap. The Plus plan removes this limit.

Charts and dashboards in Rows are functional but not as customizable as dedicated visualization tools like Tableau or Looker. If you need highly polished presentation-ready visuals, you may want to export the processed data and build final charts elsewhere.

If you want to skip the spreadsheet interface entirely and work with raw data files using a single prompt, tools like VSLZ handle the full pipeline from file upload to statistical analysis and charts without manual configuration.

Summary and next steps

Rows AI is a practical choice for anyone who regularly analyzes spreadsheet data but does not want to write code or learn complex formula syntax. The combination of live data connections, an AI analyst that responds to plain English, and built-in AI functions covers the most common analysis workflows in a single tool.

Start by connecting one live data source, asking the analyst a few questions about the data, and building a simple chart. Once you see how the prompt-to-output loop works, you can expand to more complex cross-table analysis and automated refresh schedules.