How to Analyze Business Data with Rows AI

Rows is a browser-based spreadsheet with a built-in AI Analyst. You upload or connect your data, then describe what you need in plain English. The AI adds columns, cleans records, runs statistical summaries, and builds charts without requiring SQL knowledge, Python scripts, or formula expertise. The workflow, from import to shareable dashboard, takes under an hour for most datasets.

What Rows AI Does Differently

Traditional spreadsheet analysis requires knowing which formula handles which task. VLOOKUP for merges, COUNTIF for grouping, pivot tables for summaries. Rows replaces that lookup cost with a prompt interface: you type what you want and the AI translates it into the appropriate operation.

The AI Analyst draws its understanding from your table's column headers and a sample of up to five rows. It does not read your entire dataset, which limits exposure of sensitive records while still giving the model enough context to infer data types, relationships, and plausible operations.

Rows maintains privacy by design. Only headers, sample rows, and basic statistics are shared with the underlying AI, and your data is never used to train third-party models. This makes it acceptable in environments where full-dataset uploads to external services are restricted.

The platform connects to more than 50 data sources including Google Analytics, Facebook Ads, Shopify, HubSpot, and Salesforce, as well as file imports for CSV, Excel, and PDF. This makes it practical for ops managers who pull from several systems and need a single place to combine and compare them.

Step 1: Set Up Your Workspace

Go to rows.com and create a free account. The free plan includes a limited number of AI executions per month, which is enough to evaluate the tool on a real dataset. Paid plans start around $20 per user per month for unlimited AI usage.

Once logged in, click "New Spreadsheet." The interface resembles a standard spreadsheet grid. The key difference is the sparkle icon in the bottom right corner, which opens the AI Analyst panel.

Step 2: Import Your Data

Rows supports three main import paths:

From a file: Click "Import" in the top toolbar and upload a CSV or Excel file. Rows reads the headers automatically and maps them to columns.

From a PDF or image: The Document-to-Table feature extracts structured rows from invoices, bank statements, or scanned reports. Upload the file and Rows parses it into a table. This is useful for finance teams that receive vendor invoices as PDFs rather than structured exports.

From a live integration: Click "Insert Table" and select from the integration library. Connecting Google Analytics pulls sessions, pageviews, and conversion data into a live table that refreshes on a schedule.

For this guide, assume you have a CSV export of sales transactions with columns for date, product name, region, quantity, unit price, and customer ID.

Step 3: Clean Messy Data with AI

Real business data is rarely clean. Common issues include inconsistent category names, blank cells, duplicate rows, and mixed date formats.

Open the AI Analyst panel by clicking the sparkle icon. Type your first prompt:

"Find and highlight all rows where the Region column is blank or contains a value that does not match: North, South, East, West."

The AI inspects the column and marks the problem rows. You can then ask it to fix them:

"Replace blank Region values with 'Unknown' and standardize 'N' and 'north' to 'North'."

For duplicates:

"Identify rows where Customer ID and Date are identical and mark them as duplicates."

This lets a non-technical analyst describe data quality rules in plain language instead of writing nested IF statements or running deduplication macros. Rows executes the operation and shows the result inline.

Step 4: Add Calculated Columns

Once the data is clean, enrich it with calculated fields.

Click an empty column header, type a name such as "Revenue," then in the first cell type "=" to trigger the AI formula mode. Describe what you want:

"= Quantity multiplied by Unit Price"

Rows generates the formula and applies it down the column. You can be more specific with conditional logic:

"= Calculate net revenue by multiplying Quantity by Unit Price, then subtracting a 12% discount if the Region is East."

The AI writes the expression and explains what it generated in the panel so you can verify the logic before accepting. This handles conditional calculations that would normally require nested IF functions.

Step 5: Run Analysis with Natural Language

With clean data and calculated columns, you can extract insights through direct prompts.

Ask the AI Analyst for summaries:

"Show me total Revenue by Region, sorted highest to lowest."

"What was the month-over-month revenue growth from January to March?"

"Which product had the highest quantity sold in Q1?"

Each prompt produces a summary table or direct answer in the panel. Click "Add to sheet" to pin the output as a new table in your spreadsheet.

For outlier detection:

"Identify transactions where Revenue is more than three standard deviations from the mean."

For cohort analysis:

"Group customers by their first purchase month and calculate total revenue per cohort."

These are queries that typically require a data analyst writing SQL or Python. In Rows, they run as natural language prompts against a live table. According to Rows, users report completing analysis that previously took a full workday in under two hours using the AI Analyst.

Step 6: Build Charts

Once you have a summary table, ask Rows to visualize it.

Select the summary table, open the AI panel, and type:

"Create a bar chart showing Revenue by Region. Use blue bars and add the title Q1 Revenue by Region."

Rows generates the chart inline. You can ask it to change the type, adjust colors, add data labels, or filter the range by describing the change in plain English.

For time-series data:

"Create a line chart showing monthly Revenue from January through March with a trend line."

Charts in Rows are embedded in the spreadsheet and update when the underlying data changes. If your data source is a live integration, the chart refreshes automatically on the schedule you configure.

Step 7: Share Results

Rows spreadsheets can be shared via link, embedded in Notion or Confluence, or published as standalone pages. Sharing options appear in the top-right corner of the editor.

For stakeholders who should not edit the data, use view-only links. For dashboards embedded in internal wikis, use the embed code from the Publish menu.

To automate the workflow, Rows supports scheduled table refreshes and can send email or Slack notifications when a specific condition is met, such as when a revenue figure drops below a threshold.

When Rows AI Reaches Its Limits

The AI Analyst performs well on clean, structured tables with descriptive column names. It handles ambiguity poorly when column headers are vague or when business logic depends on knowledge outside the dataset. Rows processes a sample of rows for context rather than the full table, so very large datasets may produce less accurate results on edge-case queries.

For analysis that requires joining many tables with complex domain logic, a purpose-built BI tool or SQL environment will give more reliable results. Rows is strongest as a quick-analysis layer for analysts who need to move fast on a single dataset without standing up infrastructure.

If you need to run the same analysis on data uploaded by end users through a product interface, VSLZ AI handles file uploads and questions through an agent that executes the full workflow from a single prompt, without the user touching a spreadsheet at all.

Practical Summary

Rows AI cuts the time between raw data and shareable insight by removing the need for formulas, SQL, or Python. The core workflow is: import your data, describe cleaning steps in plain language, add calculated columns through the formula prompt mode, run summaries and outlier detection as natural-language queries, build charts by description, and share via link or embed. The free plan covers enough executions to validate the approach on a real dataset before committing to a paid plan.