Description:

The Evaluate Sentence with DB Value step enables a FlowWright workflow to perform keyword-based business rule evaluation using a database decision table.

Unlike a traditional decision table that matches structured input values against database columns, this step searches a supplied text string for values contained within a specified database column. When a matching keyword or phrase is found, the workflow retrieves one or more mapped output values from the corresponding database record.

This approach is particularly useful for workflows that process free-form text, user-entered descriptions, support requests, emails, comments, AI-generated responses, or other unstructured content where decisions must be driven by keywords rather than exact field values.

If no matching keyword is found, optional default values can be assigned before the workflow follows the NoMatch execution path.

Typical uses include:

• Routing support tickets
• Categorizing emails
• Classifying customer feedback
• Processing AI-generated responses
• Identifying document types
• Assigning departments
• Detecting product categories
• Triggering workflow actions from keywords

By storing keywords and associated business rules within a database table, organizations can update classification logic without modifying workflow definitions.

Usage:

The Evaluate Sentence with DB Value step is typically used when workflows need to analyze unstructured text and make business decisions based on detected keywords.

During execution:

1. Read the configured decision table.
2. Retrieve the supplied search text.
3. Compare the search text against the configured database input column.
4. If a matching keyword or phrase is found, populate the configured output variables.
5. If no match exists, optionally assign default values.
6. Continue workflow execution through either the Matched or NoMatch return path.

A typical workflow might look like this:

Typical workflow scenarios include:

• Classifying incoming support tickets
• Routing customer service requests
• Detecting product names within messages
• Categorizing AI chatbot responses
• Assigning workflow owners based on keywords
• Processing email subjects
• Identifying document classifications
• Detecting business-specific terminology

Example:

Let’s build and execute the “evalKeywordDBDecisionTableDef” example.          

• Create a new process definition named “evalKeywordDBDecisionTableDef” and open it in designer mode. 
• Drag an “evalKeywordDBDecisionTable” step to the canvas. 
• Connect the dots between the “Start” and “evalKeywordDBDecisionTable” steps, as shown above. 
• Define a variable or a global variable to store the result.
• Click the "evalKeywordDBDecisionTable" step to configure its "Required" properties. Provide a name for the step. Provide a table name for the decision evaluation. Provide a search text. Provide the input column that contains the search text. Click the button to map the DB output columns. Click the Save button. Note: Click the "AI Predict" button for the Copilot to add new process steps that match your process description. 

• Click the button to map the DB output columns. Click the Add Row (+) button to insert an empty row. Enter the output DB column and the variable or global reference to map. Click the Save button. You may use the Add Row button to insert multiple DB output columns and variable references. 

• Click the "evalKeywordDBDecisionTable" step to configure its "Advanced" properties. Click the button to map the DB output columns. Click the Add Row (+) button to insert an empty row. Provide the output DB column and variable or global reference to map for no match. Click the Save button to confirm. You may use the Add Row button to insert multiple DB output columns and variable references. 

• The “Logging” configuration is necessary for documentation and also measures workflow progress and percent complete. This is achieved by configuring the step state and percent fields individually, as shown below. Configure the “Logging” using the following properties.

• Save the process definition, create a new instance, and execute it. Render the process instance. Click the process step to view its properties. The step should evaluate a sentence against a DB value, returning “MATCH” or “NO MATCH”. Below, the step properties show the values for an evaluated “MATCH”.  

• Configure the process step for a no-match evaluation. Save the process definition, create a new instance, and execute it. Render the process instance. Click the process step to view its properties. The step should evaluate a sentence against a DB value to return “MATCH” or “NO MATCH”. Below, the step properties show the values for an evaluated “NO MATCH”.  

Tips:

• Keep keyword lists concise and well organized to improve classification accuracy.
• Use descriptive database table names that clearly represent the business purpose.
• Configure meaningful default values to ensure predictable workflow behavior when no keyword matches are found.
• Periodically review keyword tables to accommodate new business terminology.
• Route the NoMatch path to manual review to continuously improve keyword coverage.
• Store search text in workflow variables when processing dynamic content from forms, emails, AI services, or external systems.
• Combine this step with Ask AI, Evaluate Multiple Expressions, DB Decision Table, Send Email, Business Rule, or Create Process Instance steps to build intelligent text-driven workflows.

Notes:

• A valid decision table name is required before the step can execute.
• The search text should contain the content to be evaluated for keyword matching.
• The configured database input column should contain the keywords or phrases used for comparison.
• Output column mappings determine which workflow variables receive the returned values.
• Default values are optional and are applied only when no keyword match is found.
• Both the Matched and NoMatch return paths should be implemented to provide complete workflow handling.
• Execution details and keyword evaluation results are recorded in the FlowWright workflow execution log.

Evaluate Sentence with DB Value vs. DB Decision Table:

Both workflow steps use database-driven business rules, but they evaluate different types of input.

As a general guideline:

• Use Evaluate Sentence with DB Value when workflows need to classify or route unstructured text based on keywords stored in a database.
• Use DB Decision Table when workflows need to evaluate structured business data against configurable decision rules.

Definition Sample:

You may download the sample workflow definition from the link provided and import it into your FlowWright environment.

Note: Verify and complete any missing configuration after importing the sample, including:

• Decision table name
• Search text
• Database input column
• Output column mappings
• Default values (if required)
• Success and alternate workflow branches
• Database access permissions

After verifying the configuration, save and publish the workflow before execution.

Click here to download the sample file.