DataZier Clean uses AI that actually understands your data—not just pattern matching.
It reads context, fixes formats, fills gaps, and catches outliers. Drop your file. Get clean data back.
Works with your favorite formats
Unlike regex tools, DataZier Clean actually reads your data to understand what it means—and fixes it intelligently.
Automatically identifies column types—phone numbers, emails, dates, currencies, addresses—and applies the right cleaning rules for each.
Finds exact and fuzzy duplicates. "John Smith" and "john smith" and "J. Smith" at the same address? We'll catch that.
Converts "01/15/2024", "15-Jan-24", and "January 15, 2024" to your preferred format automatically.
Standardizes "NY", "N.Y.", "New York", and "new york" to a consistent format. Works for countries, states, and cities.
Flags suspicious values—like a $1,000,000 order in a dataset where the average is $50. Review or auto-fix.
Intelligently fills missing values, removes empty rows, or flags them for review based on your preferences.
A two-phase approach that's fast, accurate, and cost-efficient. We don't over-engineer what can be solved simply.
First, we run your data through optimized rule-based processing. This handles the obvious stuff instantly—things that don't need AI to figure out.
Result: ~80% of your data is cleaned in milliseconds at near-zero cost.
For the remaining messy rows—the ones that regex can't solve—we bring in contextual AI. It actually understands your data, not just pattern-matches it.
Guardrails: AI is configured to never guess. If data is truly missing, it returns NULL—not a hallucination.
Review the changes, download your cleaned file, and get back to the work that actually matters. Your original file is never modified.
Upload messy data on the left, get clean data on the right. It's that simple.
| Name | State | Date | |
|---|---|---|---|
| john smith | john@ | N.Y. | 01/15/24 |
| JANE DOE | jane@email.com | California | 2024-01-16 |
| NULL | bob@test.com | tx | Jan 17, 2024 |
| Jane Doe | jane@email.com | CA | invalid |
| Name | State | Date | |
|---|---|---|---|
| John Smith | — | NY | 2024-01-15 |
| Jane Doe | jane@email.com | CA | 2024-01-16 |
| — | bob@test.com | TX | 2024-01-17 |
| Duplicate removed | |||
Integrate DataZier Clean directly into your ETL pipelines, Jupyter notebooks, or data workflows. Clean thousands of files programmatically with native Pandas and Polars support.
Stop spending your afternoons in Excel. Clean datasets that used to take 4+ hours now take under 5 minutes.
Marketing managers, analysts, and data scientists all use the same tool. Web UI for quick fixes, Python SDK for automation.
Your data is processed securely and never stored beyond the cleaning session. Files are automatically deleted after processing.
Start cleaning for free. Upgrade when you need more.