Industries

Overview

Industries We Serve

Discover who we empower to make the world safer.

Professional Practices

Private Investigators

Giving private investigators access to extensive digital information.

Risk Protection

Identifying threats with live data to aid risk management.

Insurance & Fraud

Detecting fraud and mitigating risks with real-time analysis.

Cyber Security

Discovering, assessing and mitigating potential cyber threats.

Law Professionals

Aiding legal professionals in digital evidence-gathering.

Anti-Money Laundering

Boosting AML efforts with actionable intelligence on suspicious activity.

Service Sectors

Government
Free Access

Empowering governments with swift digital identity verification.

Law Enforcement
Free Access

Providing tools for law enforcement to accurately track digital footprints.

Journalism
Free Access

Enabling journalists to authenticate sources and combat disinformation.

Non-Profits
Free Access

Helping investigative non-profits make the world a safer place.

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1.4 Email Extractor May 2026

print("\nExtracted emails:") for email in set(emails): # Remove duplicates print(email) if == " main ": main()

If you share your existing code or more details, I’ll tailor the solution exactly to your needs. 1.4 email extractor

def extract_emails_from_file(filepath): try: with open(filepath, 'r', encoding='utf-8') as file: content = file.read() return extract_emails(content) except FileNotFoundError: print(f"File not found: {filepath}") return [] }' return re.findall(pattern

import re def extract_emails(text): # Basic email regex pattern pattern = r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}' return re.findall(pattern, text) 1.4 email extractor

def main(): source = input("Enter text or file path: ") if source.endswith('.txt'): emails = extract_emails_from_file(source) else: emails = extract_emails(source)

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