AI Data Analysis Tool
Upload a CSV, Excel, or JSON file, describe what you want to know in plain English, and receive summary statistics, trend breakdowns, segment comparisons, and ready-to-share charts — no formulas, no code, no pivot tables required. Free to start.
How it works
Upload your dataset
Drop in a CSV, Excel, or JSON file directly in the browser — no account setup needed to get started. The tool reads column names, data types, and row counts automatically.
Describe your question in plain English
Type what you actually want to know — 'which region had the fastest growth?' or 'are there outliers in my expense data?' — rather than specifying formulas or chart types manually.
AI parses, computes, and segments
The AI identifies the relevant columns, runs summary statistics and group comparisons, detects trends or anomalies, and structures findings around your specific question.
Review results and export
You get a written analysis with key numbers, a chart recommendation (or rendered chart), and segment breakdowns you can copy, screenshot, or export to share with teammates.
Who is this for
Business analysts without SQL access
When the data team is backed up and you need quick answers from an exported report or CRM download, this lets you run segment comparisons and trend analysis on the spot without waiting for a dashboard update.
Small business owners reviewing their own numbers
If you export monthly sales, inventory, or customer records to a spreadsheet but rarely go beyond SUM and AVERAGE, plain-English analysis surfaces the patterns — best sellers, slowdown months, top customer segments — that manual scanning misses.
Researchers and students handling survey data
Survey exports from tools like Google Forms or Qualtrics land as messy CSVs. Describing your research question directly lets you get cross-tabulations, response distributions, and correlation checks without learning R or Python first.
Six prompt-engineering tips that move the needle
Small changes in how you write a prompt make the biggest difference in output.
Name the column you care about most
Instead of 'analyze my sales data,' try 'analyze the Revenue column by Region.' Referencing actual column names from your file steers the AI toward the right variables immediately.
Specify a time range if your data has dates
Phrases like 'between January and June 2024' or 'the last 90 days of records' help the tool filter correctly and produce trend lines with the right granularity.
Ask for a comparison, not just a summary
Questions framed as comparisons — 'how does Channel A perform versus Channel B on conversion rate?' — yield more actionable outputs than open-ended requests like 'summarize the data.'
Request a specific output format
Adding 'as a bullet list of top 5 findings' or 'with a bar chart' at the end of your question shapes how results are presented, making them easier to paste directly into a report or slide.
Mention the metric that defines success
Tell the tool what 'good' looks like: 'highlight any weeks where churn exceeded 5%' or 'flag products with a margin below 20%.' Thresholds turn generic stats into prioritized findings.
Follow up to drill deeper
After a first-pass summary, ask targeted follow-ups: 'break down that revenue figure by customer size' or 'which outliers are driving the spike you identified?' Iterating in conversation typically surfaces the most useful insights.
What to expect
Most datasets under 10,000 rows return a full analysis — statistics, segments, and chart recommendations — within 15–30 seconds. Very wide datasets (50+ columns) may take closer to a minute as the AI narrows down which fields are relevant to your question.
Example: A 3,200-row e-commerce order export (columns: date, product category, region, order value, return status) analyzed with the question 'which product categories have the highest return rates by region?' returned: average return rate per category (Apparel: 18%, Electronics: 9%, Home: 6%), a regional breakdown showing the Northeast had 22% higher apparel returns than other regions, and a grouped bar chart recommendation — all in under 20 seconds.
Good to know
- Causal conclusions are not reliable: the tool identifies correlations and patterns, but inferring why something happened (e.g., whether a promotion caused a sales spike) requires domain knowledge the AI doesn't have.
- Datasets with heavily nested JSON structures or merged Excel cells often need manual flattening before the column detection works correctly.
- Analysis quality degrades noticeably when key columns contain more than roughly 15–20% missing values — filling or documenting gaps before uploading produces materially better results.
Frequently asked questions
Which file formats does the tool accept?
CSV, Excel (.xlsx and .xls), and JSON files are all supported. Files up to around 50 MB work well; very large datasets may need to be trimmed to the most relevant columns or rows before uploading.
Do I need to clean my data before uploading?
Basic cleaning is handled automatically — the tool typically detects and flags missing values, duplicate rows, and mismatched data types. However, fixing domain-specific errors (like incorrect product codes) still benefits from a quick manual review beforehand.
What kinds of analysis can it actually produce?
Descriptive statistics (mean, median, percentiles, distributions), trend lines over time, group-by segment comparisons, outlier detection, and correlation analysis are all within scope. Causal inference or predictive modeling are outside its current range.
How accurate are the summary statistics?
Numeric calculations — counts, sums, averages — are highly reliable. Interpreted insights like 'this segment is underperforming' are AI-generated and should be treated as a starting hypothesis to verify, not a definitive conclusion.
Can it handle datasets with dozens of columns?
Yes. When you describe your question in plain English, the tool focuses on the columns most relevant to your query rather than trying to summarize every field, which keeps results readable and targeted.
What chart types does it recommend or generate?
Bar, line, scatter, and pie charts are commonly suggested based on your data type and question. For time-series questions you typically get line charts; for comparisons across categories, grouped bar charts are the default recommendation.
Is my uploaded data stored or shared?
Uploaded files are processed in-session for analysis and are not used to train models or shared with third parties. For sensitive datasets, removing personally identifiable columns before uploading is a sensible precaution regardless of any tool you use.
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