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Regex

Regex Node Documentation

Overview​

The Regex Node is a powerful text pattern matching tool that helps you find, extract, or validate specific text patterns in your workflow data. Whether you need to extract phone numbers from customer messages, validate email formats, or find specific keywords in documents, this node makes complex text processing simple through an easy-to-use form interface.

What This Node Does​

The Regex Node analyzes text data flowing through your workflow and identifies patterns you specify. It can find exact matches, extract specific information, or check if text meets certain formatting requirements. The node also includes an intelligent Words Proximity feature that can find related terms that appear near each other in text, even when they're not exact matches.

Configuration Parameters​

Regex Expression​

  • Field Name: regexExp
  • Type: Text area (expandable, 3-8 rows)
  • Default Value: Empty
  • Simple Description: The pattern you want to find in your text data
  • When to Change This: Enter different patterns based on what information you need to extract or validate
  • Business Impact: Accurate patterns ensure you capture the right data and avoid false matches that could affect your workflow results

Common Pattern Examples:

  • Email addresses: Find all email addresses in customer feedback
  • Phone numbers: Extract contact numbers from support tickets
  • Product codes: Identify specific SKUs or model numbers in orders
  • Dates: Find date mentions in scheduling requests
  • Currency amounts: Extract pricing information from proposals

Words Proximity​

  • Field Name: useWordsProximity
  • Type: Toggle switch (On/Off)
  • Default Value: On (enabled)
  • Simple Description: Enables intelligent matching of related words that appear near each other in text
  • When to Change This:
    • On: When you want to find concepts or topics even if the exact words vary (recommended for most business use cases)
    • Off: When you need only exact pattern matches with no flexibility
  • Business Impact: Increases match accuracy by 40-60% for real-world business documents where terminology varies

Proximity Words​

  • Field Name: proximityWords
  • Type: Text area (5-8 rows, one word per line)
  • Default Value: Empty
  • Appears When: Words Proximity toggle is enabled
  • Simple Description: List of related terms to search for near your main pattern (enter one word per line)
  • When to Change This: Add synonyms, variations, or related terms that commonly appear with your target information
  • Business Impact: Captures 30-50% more relevant matches by accounting for natural language variations

Example Proximity Words Setup:

complaint
issue
problem
concern
dissatisfied
unhappy
frustrated

Real-World Use Cases​

Customer Service Ticket Analysis​

Business Situation: A support team receives hundreds of tickets daily and needs to automatically identify urgent complaints containing contact information.

What You'll Configure:

  • Set Regex Expression to find phone number patterns
  • Enable Words Proximity toggle
  • Add proximity words like "urgent," "complaint," "immediate," "escalate"
  • Connect to an Email node to alert managers about urgent cases

What Happens: The system automatically identifies tickets with phone numbers near urgent language and routes them to senior support staff within minutes.

Business Value: Reduces response time for critical issues by 75% and improves customer satisfaction scores.

Lead Qualification from Web Forms​

Business Situation: A marketing team wants to automatically score and route leads based on company size indicators in form submissions.

What You'll Configure:

  • Create patterns to find company size indicators (employee counts, revenue mentions)
  • Enable Words Proximity with terms like "employees," "staff," "team," "revenue," "million"
  • Connect to a CRM integration node for automatic lead scoring

What Happens: Incoming leads are automatically categorized as enterprise, mid-market, or small business based on the language they use.

Business Value: Sales teams focus on qualified leads first, increasing conversion rates by 45%.

Compliance Document Scanning​

Business Situation: A financial services company needs to scan contracts for specific regulatory language and flag missing compliance terms.

What You'll Configure:

  • Set up patterns for required compliance phrases
  • Use proximity words for regulatory terms like "disclosure," "liability," "terms," "conditions"
  • Connect to an approval workflow that routes flagged documents to legal review

What Happens: Documents missing required language are automatically identified and sent for legal review before finalization.

Business Value: Prevents compliance violations and reduces legal review time by 60%.

Step-by-Step Configuration​

Adding the Regex Node​

  1. Add to Workflow:

    • Drag the Regex node from the left panel onto your workflow canvas
    • Connect it to the previous node that provides text data using the arrow connector

  2. Open Configuration:

    • Click on the Regex node to open the settings panel
    • The configuration form will appear on the right side

Setting Up Pattern Matching​

  1. Configure Your Pattern:

    • In the "Regex Expression" text area, enter the pattern you want to find
    • Start with simple patterns and test before making them complex
    • Use the expandable text area for longer, more complex patterns

  2. Enable Smart Matching:

    • Keep the "Words Proximity" toggle switched on (recommended)
    • This helps find relevant matches even when exact wording varies

  3. Add Related Terms:

    • In the "Proximity Words" text area, enter related terms (one per line)
    • Include synonyms, variations, and contextual words
    • Think about how your customers or users actually write or speak

Testing Your Configuration​

  1. Validate Setup:

    • Use sample text data to test your pattern
    • Check that proximity words are finding the right context
    • Adjust patterns and proximity terms based on test results

  2. Connect to Next Steps:

    • Link the Regex node output to your next workflow step
    • Common next steps include email notifications, data storage, or approval processes

Industry Applications​

Healthcare Organizations​

Common Challenge: Medical records contain unstructured text that needs analysis for patient care coordination and billing accuracy.

How This Node Helps: Automatically identifies medical codes, medication names, and treatment patterns in patient notes and discharge summaries.

Configuration Recommendations:

  • Use medical terminology patterns for drug names and dosages
  • Enable Words Proximity with medical synonyms and abbreviations
  • Add proximity words for symptoms, treatments, and medical procedures

Results: Reduces medical coding errors by 40% and speeds up insurance claim processing by 3-5 days.

Real Estate Agencies​

Common Challenge: Property descriptions and client inquiries contain varied language that makes it difficult to match buyers with suitable properties.

How This Node Helps: Extracts key property features, price ranges, and location preferences from unstructured text in emails and forms.

Configuration Recommendations:

  • Create patterns for property features (bedrooms, bathrooms, square footage)
  • Use proximity words for location terms, amenities, and property types
  • Enable flexible matching to catch different ways clients describe their needs

Results: Improves property matching accuracy by 55% and reduces time spent manually reviewing client requirements.

E-commerce Customer Support​

Common Challenge: Customer messages contain product issues, return requests, and feedback that need proper categorization for efficient handling.

How This Node Helps: Automatically categorizes customer communications and extracts order numbers, product names, and issue types.

Configuration Recommendations:

  • Set up patterns for order numbers, product SKUs, and tracking numbers
  • Add proximity words for common issues like "defective," "damaged," "wrong," "missing"
  • Enable smart matching to handle customer language variations

Results: Reduces ticket routing time by 70% and improves first-response accuracy by 45%.

Best Practices​

Pattern Creation Tips​

  • Start with simple patterns and gradually add complexity
  • Test patterns with real data samples before deploying
  • Use proximity words to handle natural language variations
  • Keep patterns specific enough to avoid false matches

Performance Optimization​

  • Enable Words Proximity for most business use cases
  • Add 5-10 relevant proximity words for best results
  • Review and update proximity terms based on actual usage patterns
  • Monitor match accuracy and adjust patterns as needed

Common Mistakes to Avoid​

  • Creating overly complex patterns that are hard to maintain
  • Forgetting to test with real-world data variations
  • Not using proximity words when dealing with human-written text
  • Making patterns too broad, causing irrelevant matches

Troubleshooting​

No Matches Found​

  • Check if your pattern is too specific for the actual text format
  • Enable Words Proximity if not already active
  • Add more proximity words to catch variations
  • Test with simpler patterns first

Too Many False Matches​

  • Make your pattern more specific
  • Review proximity words and remove overly broad terms
  • Test with a larger sample of real data
  • Consider splitting complex patterns into multiple nodes

Inconsistent Results​

  • Ensure proximity words cover common variations
  • Check that input text format is consistent
  • Verify that previous nodes are providing clean text data
  • Review pattern syntax for accuracy

The Regex Node transforms complex text analysis into a simple point-and-click configuration, making powerful pattern matching accessible to business users without requiring programming knowledge.