Extract Emails, Phone Numbers & SSNs From CSV — Without Uploading Them

Online tools upload your data

Every "free" online extractor sends your file to a server. For PII — emails, phone numbers, SSNs, credit cards — that's a HIPAA violation waiting to happen.

Python scripts take 20+ minutes to write

You need to write the regex, handle encoding issues, test edge cases, validate the output, and debug the script. That's 20–30 minutes every time — if you even know Python.

grep misses validation entirely

grep finds patterns, but it can't tell you if a credit card number passes the Luhn algorithm, or if a phone number is a real US number vs. a random 10-digit string.

A raw regex matches "1234567890" as a valid phone number. Balanced mode applies NANP rules — a real US number needs a full 10 digits and an area code that doesn't start with 0 or 1.

• Fast for simple text searches on local files
• Requires writing and escaping regex patterns manually
• No CSV-awareness — treats commas as text, not delimiters
• Zero validation — matches any sequence that fits the pattern
• No deduplication, no normalization, no export formatting

• Full control — you write exactly what you need
• 20–30 min to write, test, and debug a single extraction script
• Luhn and SSA validation require additional libraries or manual code
• Data still lives on your machine, but the barrier to entry is high
• Every new file or pattern type requires code modifications

• No coding required — paste or upload your file
• File contents transmitted to and processed on a remote server
• No Luhn or SSA validation in any tool we found
• HIPAA, GDPR, and PCI-DSS violations possible for regulated data

• Upload-free: file contents never leave your browser tab
• No coding required — point-and-click column targeting
• Luhn algorithm built in for credit cards
• SSA structural rules built in for SSNs
• Three precision modes: permissive, balanced, strict
• Context window, deduplication, and normalization included
• Export to CSV or JSON in one click

Email List Cleanup

Phone Number Extraction

Credit Card Audit

Perfect for

• Compliance and legal teams auditing PII in existing datasets
• Analysts extracting contact data from notes or freeform fields
• Healthcare teams processing patient records under HIPAA constraints
• Finance teams auditing transaction logs for exposed card numbers
• Non-coders who need regex-level results without writing any code
• Teams in regulated industries where file uploads are prohibited
• One-off extractions that don't justify writing a Python script
• QA teams validating that test datasets don't contain real PII
• Anyone who needs to normalize phone numbers or dates to a standard format

Not ideal for

• Non-US phone numbers: The phone engine matches US/NANP 10-digit numbers only. International numbers are not matched or normalized.
• Custom pattern types: You can only extract the 8 built-in types. Custom regex patterns are not supported — use grep or Python for those.
• Excel files over 200MB: Excel (.xlsx) is fully loaded into memory by SheetJS, so it's capped at 200MB. Convert large Excel to CSV first — CSV streams to disk and has no practical size limit.
• Encoded or encrypted data: The tool works on plaintext. Hash-encoded, base64-encoded, or encrypted PII will not be detected.
• Real-time streaming data: This is a file-based batch tool. It is not designed for streaming pipelines or API-based processing.

Zero-Upload Architecture: What It Actually Means

Not 'we don't store your data' — we never receive it. Your file content is parsed entirely inside your browser tab using a Web Worker. Nothing is transmitted over the network.

Time & Cost Savings Calculator

Estimate your annual savings vs. writing Python extraction scripts

1–8 pattern types

e.g., 52 = weekly

Used to calculate dollar savings