AI Data Analysis Tool

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.

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.

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.

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.

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.

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.