⚡ Deterministic spreadsheet analysis

Verify your spreadsheets before sharing them.

Upload your spreadsheet and let SheetVerify detect missing values, duplicates, structural problems, formula inconsistencies, and totals mismatches with a clear, reliable report.

Upload Spreadsheet

Supports .xlsx and .csv files.

⇪

Drop your spreadsheet here or click to browse

Run a pre-share reliability check in seconds.

Supports .xlsx and .csv files up to 10 MB.

Advanced Detection Settings

Optional tuning for threshold-based detectors. Leave these unchanged to use SheetVerify’s default behavior.

Missing Value Threshold

0% = flag every missing value. 5% = default.

Formula Consistency Dominant-Pattern Ratio

Higher values require stronger formula consistency before deviations are flagged. 75% = default.

Mixed Data Type Minority Ratio

Lower values flag smaller type inconsistencies. Higher values ignore small minority outliers. 20% = default.

Totals Mismatch Tolerance

0% = strict total matching. 0.1% = default.

Try Demo

No setup required. Upload your own file or try the built-in demo spreadsheet.

Privacy Note

Uploaded spreadsheets are processed to generate the reliability report and are not permanently stored.

To support report generation and system reliability, limited metadata about the scan (such as detected issue details and structural information) may be temporarily stored.

Please avoid uploading sensitive personal or confidential data.

Files are processed automatically and are not used for training AI models.

What SheetVerify Detects

Common spreadsheet issues caught by our validation engine.

Duplicate Values

Catch repeated emails, IDs, and fields that should often remain unique across the sheet.

Formula Issues

Surface broken consistency patterns and spreadsheet logic that may silently damage outputs.

Missing Values

Flag empty cells in important columns before they break reporting, filtering, or handoff workflows.

Duplicate Rows

Find repeated records that can distort totals, inflate analysis, and create downstream confusion.

Mixed Data Types

Detect inconsistent values in a column, such as numbers mixed with text or irregular formats.

Totals Mismatches

Catch rows where totals do not match the values they are supposed to summarize.

Output Example

See what your reliability report will look like.

✓ Validation completed in 1.24s

Reliability Score: 87 / 100

Issues Found: 4

HIGH: 1

MEDIUM: 2

LOW: 1

▲ HIGH: Duplicate email detected in key column

▲ MEDIUM: Totals mismatch found in summary row

▲ LOW: Missing optional field detected in notes column