Extract Emails, Phone Numbers & SSNs From CSV — Without Uploading Them
Pull emails, phone numbers, credit cards, SSNs, URLs, dates, IP addresses, and ZIP codes from any CSV or text file — with validation, normalization, and zero uploads. The only pattern extractor built for data that cannot leave your machine.
Used by data analysts, compliance teams, and auditors handling regulated datasets.
Three reasons the current options don't work
Online tools require an upload
Every web-based extractor wants your file. Customer lists. Medical records. Financial exports. Uploading PII to a third-party server is a compliance violation — not a workflow.
Python regex requires coding
Writing re.findall() patterns that correctly handle edge cases — Luhn validation, international phone formats, date ambiguity — takes 20–30 minutes per pattern type, and breaks when data is dirty.
grep misses edge cases silently
grep finds strings that look like patterns. It does not validate them. 078-05-1120 passes a 9-digit SSN regex. 4111111111111112 fails Luhn but matches the CC pattern. You get results with unknown accuracy.
Regex-only extraction produces 30–40% false positives in unstructured text fields (internal testing, Feb 2026). Varies by dataset and field type.
What Makes This Different From a Regex Script
This is not just regex wrapped in a UI.
8 Pattern Types With Real Validation
Email (RFC 5322), Phone (NANP + international), URL, Date (12+ formats), Credit Card (Luhn), SSN (SSA rules), IP Address, ZIP Code — not just regexes, but validators.
Three Validation Modes
Permissive (widest net), Balanced (format rules applied), Strict (deep validation — Luhn, SSA, area codes). Match mode to your use case.
Post-Extraction Normalization
Phone to E.164 (+15551234567), email to lowercase, date to ISO 8601, URL protocol enforcement — applied after extraction, adds minimal overhead.
Column-Level Targeting
For CSV and Excel files, select exactly which columns to scan. Eliminates noise from scanning columns that cannot contain the patterns you need.
Context View Per Match
100 characters of surrounding text shown per match with the pattern highlighted. Trace exactly where each value came from.
Sensitive Data Masking in UI
Credit card numbers and SSNs are masked in the results table (last 4 visible only). Context view obscures mid-digits. Export controls let you decide whether to include full values.
5M+ Rows Supported
5M rows processed in 45 seconds (Chrome 131, Windows 11, i7-12700K, 32GB RAM, Feb 2026). Results vary by hardware, browser, and pattern complexity.
Privacy by Architecture, Not Policy
File contents never leave your browser. No server receives your data at any point. HIPAA, GDPR, PCI-DSS, and SOX compliance is structural — there is no data transmission to govern.
SplitForge vs. The Alternatives
When you're extracting PII from production data, the tool choice is a compliance decision
| Capability | grep / awk | Python re / pandas | Online Extractors | SplitForge |
|---|---|---|---|---|
| No upload required (PII-safe) | Local | Local | Uploads file | Browser-only |
| Requires no coding | CLI required | Python required | Point and click | No code needed |
| Luhn CC validation | No | Write it yourself | Varies by tool | Built-in |
| SSA-rule SSN validation | No | Write it yourself | No | Built-in |
| Post-extraction normalization | No | Write it yourself | No | E.164, ISO, etc. |
| Context view per match | grep -C flag | Manual with span() | No | Built-in, 100 chars |
| Sensitive data masking in UI | Plain text output | Plain text output | Plain text output | CC + SSN masked |
| Column-level targeting | awk field selector | df column access | Scans entire file | Column grid UI |
| Multi-sheet Excel support | No | openpyxl required | Usually no | Yes, sheet selector |
| File size limit | Unlimited | RAM-bound | 10–50MB typical | 1GB+ tested |
Which Tool Is Right for You?
No single tool is right for every situation. Here's an honest breakdown.
Use grep / awk if:
- You need automation in a shell script or CI pipeline
- You're comfortable with regex and command-line tools
- The data is not PII (log files, URLs, non-sensitive identifiers)
- Speed on very large files (>10GB) matters more than validation accuracy
- You don't need a UI or formatted export — raw output is fine
Use Python re / pandas if:
- You need to run this on a schedule or inside a data pipeline
- You need custom regex patterns beyond the 8 built-in types
- You're processing 50M+ row files where browser limits apply
- You're comfortable writing and maintaining Python code
- Extraction is part of a larger ETL or transformation workflow
Use online extractors only if:
- The data contains no PII, PHI, or sensitive business information
- Your organization has no data residency or compliance requirements
- The file is under the tool's size limit (typically 10–50MB)
- You've reviewed the tool's data retention and deletion policies
Use SplitForge if:
- Your data contains PII, PHI, or regulated financial information
- You don't write Python — or don't want to for a one-off extraction task
- You need Luhn, SSA, or area code validation without writing it yourself
- You want normalization (E.164, ISO dates) in the same step as extraction
- You need context view for audit trails or compliance review
- File size exceeds online tool limits but stays under ~1GB
- You process this type of file regularly and want a consistent, repeatable workflow
Real-World Use Cases
CRM Data Audit
Healthcare Records De-identification
E-commerce Fraud Review
Edge Cases That Break Simple Regex
Overlapping Patterns (Email Inside a URL)
Handles patterns nested inside each other without duplicate false matches
International Phone Number Formats
Detects 50+ country formats without misclassifying non-phone numbers
Date Ambiguity (MM/DD vs DD/MM)
Identifies ambiguous dates and shows confidence rather than silently guessing
Credit Card Numbers in Free Text
Luhn algorithm validation eliminates false positives from random number sequences
SSN Validation Against SSA Rules
Validates against SSA publication rules, not just the 9-digit pattern
When to Use Pattern Extraction — And When Not To
Built for these workflows
- HIPAA de-identification audits: finding PHI in free-text clinical notes
- CRM data quality: extracting contact info from unstructured fields before import
- PCI-DSS compliance: locating card numbers in transaction exports or comment fields
- GDPR data subject requests: identifying all personal data in a customer export
- HR data cleanup: extracting phone and email from legacy free-text records
- Marketing list validation: extracting and normalizing emails before sending
- Support ticket analysis: pulling contact info from ticket body text at scale
- Financial audit prep: identifying SSNs or account numbers in unstructured fields
- E-commerce fraud review: finding card numbers or IDs in order comments
Honest limitations
- ~1GB browser ceiling — files larger than this require Python or server-side tools
- No custom regex — 8 built-in types only; user-defined patterns not yet supported
- No automation or API — cannot run on a schedule or inside a data pipeline
- One file per session — no batch scanning across multiple files simultaneously
- 1M pattern result cap — very dense files may hit this ceiling before scan completes
Security Architecture
Privacy by architecture, not policy — what that actually means
How Much Time Does Scripting Each Extraction Cost You?
Calculate your annual time savings vs. writing Python regex scripts per task
Typical: 2–4 types (email + phone + date)
Monthly = 12, Weekly = 52
Analyst avg: $45–75/hr
551,646 Patterns Extracted in 45 Seconds
5 million rows, 6 pattern types active, balanced validation mode — entirely in your browser with zero server transmission.
Operation: Balanced validation, 6 pattern types active, deduplication off
Method: 10 runs, highest/lowest discarded, remaining 8 averaged
Variance: Results vary by hardware, browser, pattern count, and validation mode (±15–20%)
Frequently Asked Questions
Is my data private?
What file types and sizes are supported?
What are the 8 pattern types?
What are the validation modes?
What does normalization do?
Can I extract patterns from a specific column?
What does context view show?
What export formats are available?
Does deduplication affect performance?
What are the limitations?
What browsers are supported?
Extract Sensitive Data. Keep It That Way.
8 pattern types. Luhn + SSA validation. Normalization to E.164 and ISO formats. Results stay in your browser. File contents never transmitted.
Also try: Data Masking · Data Cleaner · Data Validator · Remove Duplicates