Helvia.ai Release 5.73.0

12 March 2025

1. NLP Customization and Tag Configuration for KB Articles and Automated Answers

Our latest feature set empowers bot admins with unparalleled control over chatbot training and content organization. You can now create multiple NLP systems tailored to specific content categories, ensuring the most accurate and relevant responses for your users. This feature allows you to train individual NLP systems on content selected subsets tagged with unique identifiers, optimizing the precision of automated interactions.

Additionally, we have enhanced the configuration options for tagging within Knowledge Base (KB) articles and Automated Answers. By easily assigning and managing tags, you streamline content categorization and improve the efficiency of your chatbot's response mechanisms.

Example use case scenario:

Imagine a retail brand expanding its product line into clothing, electronics, and home goods. By using the multiple NLP, the brand can deploy specialized NLP systems catered to each product category. Each system is meticulously trained to understand the nuances of customer inquiries within its category, such as size and fit for clothing, technical specifications for electronics, and material or style for home goods. Tags enable focused training, ensuring customers receive the most accurate and contextually relevant information.

This update enhances your chatbot’s ability to deliver contextually accurate responses, benefitting both administrators and end-users.

2. Introducing Static Variables

We are excited to announce the introduction of Static Variables, designed to streamline your bot's configuration processes and enhance overall performance. This new feature empowers bot admins with the ability to define and manage consistent values such as API tokens, URLs, and other reusable data directly from your bot's settings, making them easily accessible within your Flow editor.

To add a Static Variable, visit the new Static Variables tab within the bot's behavior and click the 'Add Variable' button.

Fill out a unique name, value, and optional description and click 'Submit'.

You can then start using the Static Variables across the Bot Behavior.

3. Diverse Backgrounds for Webchat Headers

With our new update, you can now personalize your Webchat header with any background of your choosing—be it images, gradients, or solid colors. Elevate your brand's presence and captivate your audience with a webchat aesthetic that truly reflects your style.

To add a custom image, you can use the below custom setting on your webchat deployment.

{
   "widgetStyleSet": {
      "header": { 
           "backgroundColor": url("http://media/examples/example.png")
       }
    }
}

4. Optimized 'Variable' Node for Improved UX

We've introduced a redesigned 'Variable' node in our Flow Editor, optimizing how bot authors set variable values within the platform. This upgrade focuses on delivering a more intuitive and streamlined user experience.

Key enhancements:

• Unified Language Editing: The 'Variable' node now supports editing only in the primary language, reducing complexity and ensuring a consistent setup process.

• User-Friendly Interface: We've restructured the variable value section to be more prominent, allowing bot authors to focus swiftly on what matters most.

• Simplified Display: To enhance clarity, math operations and variable descriptions are now less prominent, emphasizing essential information.

5. Streamlined Analytics with Automated Answer Analytics Tags

With our latest update, Automated Answers (AAs) now incorporate tags, providing you with more granular insights directly within your bot records. This allows for more precise tracking and analysis of chatbot interactions, enabling you to refine your strategies based on detailed session data.

Once set, tags from Automated Answers will be stored by default during chat sessions, for a seamless insights-gathering process. Automated Answers, whether stemming from intents or knowledge base articles, will now include Analytics tags during chat sessions. This ensures that every interaction is comprehensively tagged and available for review.

6. Enhanced User Insights with Return Rate Metric

Console Analytics now include the Return Rate metric, providing deeper insights into user engagement. By tracking the percentage of users returning to interact with your chatbot, you can better gauge customer satisfaction and enhance your strategies to improve retention.