Regex Node - Synthreo Builder

Purpose

The Regex node is a text pattern matching tool that finds, extracts, or validates specific text patterns in your workflow data. Whether you need to extract phone numbers from customer messages, validate email formats, or identify specific keywords in documents, this node handles complex text processing through a straightforward configuration interface.

The node also includes a Words Proximity feature that can find related terms appearing near each other in text, even when the phrasing varies - making it well-suited for natural language analysis in business documents.

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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 whether text meets certain formatting requirements. When Words Proximity is enabled, the node can also find conceptually related terms that appear near one another in the text, expanding match coverage beyond literal pattern hits.

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Configuration Parameters

Regex Expression

Parameter	Details
Field Name	regexExp
Type	Text area (expandable)
Default Value	Empty
Description	The regular expression pattern to match against text data in the workflow. Enter the pattern using standard regex syntax.

The regex pattern is applied to the relevant text field in each record passing through the node. Records where the pattern matches are flagged, extracted from, or passed through based on downstream node configuration.

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 order records
• Dates - find date mentions in scheduling requests or form submissions
• Currency amounts - extract pricing information from unstructured proposals

Words Proximity

Parameter	Details
Field Name	useWordsProximity
Type	Toggle switch (On/Off)
Default Value	On
Description	Enables intelligent matching of related words that appear near each other in text, in addition to the main regex pattern match.

When enabled, the node looks for occurrences of the Proximity Words appearing in close proximity to a match from the Regex Expression. This expands coverage to catch relevant context even when exact phrasing varies.

• On - Recommended for most business use cases where human-written text contains natural language variation.
• Off - Use only when you need strict pattern-only matching with no flexibility for surrounding context.

Proximity Words

Parameter	Details
Field Name	proximityWords
Type	Text area (one word per line)
Default Value	Empty
Appears when	Words Proximity toggle is enabled
Description	A list of related terms to search for near your main regex pattern. Enter one word per line. Include synonyms, variations, and contextually related terms.

Example proximity words for complaint detection:

complaint
issue
problem
concern
dissatisfied
unhappy
frustrated

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Step-by-Step Configuration

Adding the Regex Node

1. Drag the Regex node from the node panel onto your workflow canvas.
2. Connect it to the previous node that provides text data using the connector arrow.
3. Click on the Regex node to open the configuration panel.

Setting Up Pattern Matching

1. In the Regex Expression text area, enter the regular expression pattern you want to match.
2. Start with a simple pattern and test it before making it more complex.
3. Use the expandable text area for longer or multi-line patterns.

Enabling Smart Matching

1. Keep the Words Proximity toggle enabled (recommended for most use cases).
2. In the Proximity Words text area, enter related terms - one per line.
3. Include synonyms, abbreviations, and contextual terms that commonly appear near the pattern you are searching for.
4. Think about the actual language your users or source systems use when writing the text you are processing.

Testing Your Configuration

1. Use sample text data to verify that your pattern matches the expected content.
2. Confirm that proximity words are capturing the right contextual matches.
3. Adjust patterns and proximity terms based on test results.
4. Connect the Regex node output to the next workflow step once validated.

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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 that contain customer contact information.

What You’ll Configure:

• Set Regex Expression to a phone number pattern.
• Enable Words Proximity.
• Add proximity words such as urgent, complaint, immediate, escalate.
• Connect to a routing or notification node to alert managers about urgent cases.

What Happens: The system automatically identifies tickets where a phone number appears near urgent language and routes those records to senior support staff.

Lead Qualification from Web Forms

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

What You’ll Configure:

• Create patterns to find company size indicators (employee counts, revenue figures).
• 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 based on the company size language used in the submission, and routed to the appropriate sales team.

Compliance Document Scanning

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

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.

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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 procedure names.

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 inquiry forms.

Configuration Recommendations:

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

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 natural language variation in customer messages.

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Best Practices

Pattern Creation

• Start with simple patterns and gradually add complexity as you verify behavior with real data.
• Test patterns with actual data samples before deploying to production.
• Use proximity words to handle natural language variation rather than trying to enumerate every possible phrase in the regex itself.
• Keep patterns specific enough to avoid false positives, but not so specific that minor formatting differences cause misses.

Performance Optimization

• Enable Words Proximity for most business use cases involving human-written text.
• Use 5 to 10 relevant proximity words for best coverage without over-broadening matches.
• Review and update proximity terms periodically as your understanding of source text patterns improves.
• Monitor match accuracy in production and adjust patterns and proximity words based on actual results.

Common Mistakes to Avoid

• Creating overly complex patterns that are difficult to maintain or debug.
• Failing to test with real-world data that includes formatting variation (extra spaces, different delimiters, mixed case).
• Not using proximity words when processing natural language text where exact phrasing is unpredictable.
• Making patterns too broad, causing irrelevant records to match and pollute downstream processing.

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Troubleshooting

No matches found

The pattern may be too specific for the actual text format in your data. Try enabling Words Proximity if it is not already active. Add more proximity words to catch variations. Test with simpler patterns first to isolate whether the issue is the regex syntax or the data itself.

Too many false matches

Make the pattern more specific. Review the proximity words list and remove overly broad terms. Test with a larger and more representative sample of real data. Consider splitting complex matching logic across multiple Regex nodes, each targeting a specific use case.

Inconsistent results across records

Confirm that proximity words cover common variations in your source data. Check that input text format is consistent - if upstream nodes produce text with varying structure, some records may match while others do not. Verify that the node receiving text from prior nodes is providing clean, consistently formatted content.

Pattern works in an external regex tester but not in the node

Regex engine implementations differ slightly. Confirm that the pattern syntax is compatible with the regex engine used by this node. Avoid advanced features that may not be supported. Test directly in the workflow using the node’s test functionality rather than relying solely on external tools.

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Related Nodes

• String Operation - For simpler text transformations such as replace, trim, and split that do not require full regex pattern matching.
• Filter By - For filtering records by field value conditions after regex extraction.
• Custom Script - For complex pattern matching logic that requires programmatic control beyond what the Regex node configuration supports.
• Set Transformation - For reshaping or grouping records after regex-based identification or extraction.