Extract Complete Contact Information From Any Text
Extract emails, names, phones, companies, and addresses from any text at once.
100% private. All extraction runs in your browser — your text never leaves your device.
Contact Info Extractor — Email, Name, Phone, Company, Address
Paste any unstructured text — email signatures, HTML directories, CSV exports, copied LinkedIn pages — and extract all five contact fields simultaneously in one pass. The extractor groups related fields into contact records, shows field coverage with a completeness chart, and exports as CSV (for CRM import), JSON (for developers), or vCard .vcf (for address books). All processing is client-side and private.
Tips for best results
- Separate multiple contacts with blank lines — the extractor groups fields within each block
- Email signatures with consistent formatting produce the highest accuracy
- The confidence score (0–100) shows how complete each record is — review scores below 60
- Company names work best when they appear on a dedicated line or after "at", "from", or "|"
- For HTML source, paste the full source — the extractor strips tags before parsing
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Contact information is scattered everywhere — in email signatures, on business cards photographed and OCR'd, in HTML source of company directories, in LinkedIn profile exports, in CSV files from trade shows or webinars, in copied web content. Pulling all the structured contact fields manually — email, name, phone, company, address — from unstructured text is tedious, slow, and error-prone at any meaningful volume. The Contact Info Extractor does all of this in a single pass. Paste any amount of text — a block of email signatures, a directory listing, a CSV export, raw HTML, or a copied LinkedIn page — and the tool simultaneously extracts: email addresses (RFC 5322 validated), phone numbers (domestic and international formats), full names (using capitalization and common name patterns), company names (following name patterns or after keywords like "at", "from", "@company"), and physical addresses (street number + street name patterns with city/state/zip). Every extracted piece of information is associated with the nearest contact block, and the results are presented as a structured contact table — one row per identified contact — that you can filter, sort, and export as CSV or JSON. This is the tool for turning unstructured contact data into a clean, structured contact list without manual copy-paste work. All processing runs entirely in your browser — your content never leaves your device.
- ·Name extraction uses capitalization heuristics — works best with standard English-language business text
- ·Contact grouping based on proximity within 10 lines of each other in the source text
- ·Company detection uses keyword proximity and domain inference — not a business name database lookup
- ·All processing is client-side — no content is transmitted to any server
- →Extracting contact details from a batch of email signatures to build a CRM import
- →Parsing a company directory page's HTML source into a structured contact table
- →Converting trade show badge scan exports or OCR results into clean contact records
- →Extracting all contact information from a LinkedIn Sales Navigator export or similar
- →Pulling structured contact data from a mixed CSV or spreadsheet with inconsistent formatting
- →Building a lead list from scraped or copied content that contains contact information in prose
Marcus manages business development and receives a batch of 85 email signatures from a conference. He copies all of them into the extractor. In seconds: 82 emails extracted, 67 names parsed, 71 phone numbers identified, 54 company names detected, 23 addresses found. The tool produces a structured table with one row per contact, showing which fields were found for each. He exports as CSV and imports directly to HubSpot — a task that would have taken 3 hours manually took 90 seconds.
How does name extraction work without machine learning?
The extractor uses pattern matching based on capitalization rules: words that are capitalized and not known common words (the, at, and, from, etc.) adjacent to email addresses or after greeting patterns (Dear, Hi, Hello, From, Name:) are treated as potential names. First and last name pairs (two capitalized words) score higher confidence. The pattern works well for English-language business contact text but may miss names in all-caps or all-lowercase text.
How does company extraction work?
Company names are detected using several signals: the text following 'at', 'from', 'with', '@', '|', or '·' after a name, text on lines containing 'Company:', 'Organization:', 'Employer:', 'From:', or 'Corp', 'Inc', 'LLC', 'Ltd', 'GmbH' suffixes. The extractor also recognizes domain-like patterns in email addresses and uses the domain as a likely company name clue.
What address formats are recognized?
The extractor identifies US-format addresses: a street number followed by a street name, optionally followed by a city, state abbreviation, and/or ZIP code. International addresses are not specifically parsed but may be partially captured. Addresses embedded in HTML (like those in structured data or vCard format) are extracted from their attributes directly.
How accurate is the contact grouping?
Contact grouping — associating the right name, phone, company, and address with the right email — is the hardest part of contact extraction. The extractor groups fields that appear within close proximity (typically within 5–10 lines of each other). For well-formatted email signatures and directories, grouping accuracy is high. For densely mixed text with multiple contacts per paragraph, some fields may be associated with the wrong contact. Review the table before importing to a CRM.
Can I extract contacts from HTML source?
Yes — paste the HTML source directly. The extractor strips HTML tags before parsing, so the text content is analyzed. For structured directories (with vCard microdata, schema.org Person markup, or consistent HTML class patterns), extraction accuracy is higher than for plain prose HTML.
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