Lira

What is Lira

It is a Generative AI supporting companies in optimizing their work routines. With its advanced technology, it goes beyond traditional AIs, as it is capable of reading, interpreting, and extracting valuable insights from internal documents and websites that contain FAQs or system documentation.

How to Activate

To start using it, you will need to:

1. Contact your CS so they can adjust the commercial conditions and processes in the Billings system.

2. The CS must open a ticket for Omni, informing the org and users who should have access so that the module can be released.

How to enable LIRA’s features

After activation, the profile with Lira’s permissions will be added to the organization, making it possible to grant access to other users. As shown in the following image:

NOTE: This profile has only one permission, which is access to Lira. Therefore, we do not recommend changing or deleting this profile.

How to configure

Access the Lira Menu Prompt, where it will be possible to manage the files for Lira’s processing.

In this Prompt submenu, two types of files can be inserted for analysis by the generative assistant:

• URL: A URL can be inserted (FAQ, Wikipedia, or sites such as Omni’s own documentation for analysis, where the text will be used for the generative assistant’s analysis).

• File: A file containing text can be uploaded, which will be used for the generative assistant’s analysis.

After inserting, simply click the Update button and wait for processing.

Multiple insertions are possible, so by clicking the '+' button you can insert more files, with a maximum of 5 files/URLs. This insertion is per organization.
It is also possible to delete records from the list by clicking the 'Trash' button, where the data will be removed from the generative assistant after a few minutes.

How to test

After the data has been fully processed, it can be tested using the "Test" button.

As the results are generated by the generative assistant, the answers will not always be identical. However, as long as the question is identified by the generative assistant’s knowledge base, the results will be quickly returned to the user consulting Lira.

How to use

To use Lira, simply access an attendance, and in the right-hand side menu, according to the logged-in user’s permissions, select the "Lira" button. In the text box, type the question you want the generative assistant to answer.

Once the question is asked, after a short interval the answer will be displayed to the user.

Help Me Button

The Help Me feature was created to assist agents during attendance, generating automatic summaries of conversations and formulating questions that can facilitate the search for answers in Lira’s knowledge base.

Main Features

• 📝 Attendance Summary
  By clicking the Help Me button, the system automatically generates a summary of the ongoing conversation.

  • The summary includes both the client’s messages and the agent’s.
  • It highlights the main situations reported, making consultation more practical and faster.

• 🤖 Agility in Attendance
  Along with the summary, the system automatically creates a question directed to Lira, based on the context of the conversation.
  This way, Lira can suggest a useful answer, speeding up the attendance.

• 🔄 Control of New Messages

  • A new summary will only be generated if there are new interactions since the last summary.
  • If there are no new messages, the system informs that there are no additional interactions to summarize.

• 📍 Button Location
  The Help Me button is available within the attendance:

  • In the sidebar, just below the Lira button.

• 📂 Summary Availability
  All generated summaries are stored and can be consulted in the Interaction Tracking.

• 💬 Available Channels
  Currently, the feature is enabled for attendances via Chat/Boteria and WhatsApp.

Like and Dislike Button

This feature allows the user to evaluate the response generated by Lira, indicating whether it was useful or not.

At the end of each response, two buttons are displayed:

• 👍 Like — used when the answer was satisfactory.
• 👎 Dislike — used when the answer did not meet expectations.

🟢 Like

When the user clicks the Like button, the button turns green, indicating that the feedback was positive.
Right after, a thank-you message is displayed:

💬 "Thank you for your feedback!"

This record is saved in Lira’s history, and the supervisor can view it in the report as a positive evaluation (score = 1), along with the time it was submitted.

🔴 Dislike

When clicking Dislike, the button is marked in red and a similar thank-you message appears, followed by a list of dissatisfaction reasons:

• ❌ Did not find the answer
• ⚠️ Incorrect answer
• 🧩 Incomplete answer
• 🏷️ Answer for the wrong segment
• 🔄 Wrong procedure indicated

The user selects one of the reasons, and the system records this evaluation with score = 0, along with the reason and the date/time of the record.

📋 History and Reports

In Lira’s report, it is possible to consult all evaluations performed.
The message history displays only the type of evaluation (Like or Dislike), without detailing the reason.
To view the reasons, the supervisor can download the CSV of evaluations, which contains the complete information.

🧾 Example of Data in CSV

score	reason	score_time
1	—	2025-10-15 10:33:16
0	Wrong procedure indicated	2025-10-15 10:33:54
0	Did not find the answer	2025-10-15 10:34:43
0	Incomplete answer	2025-10-15 10:35:58
0	Answer for the wrong segment	2025-10-15 10:30:40

📌 Tip:

• score = 1 → Like (positive evaluation, no reason)
• score = 0 → Dislike (negative evaluation with selected reason)

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How to monitor usage and consumption

To check Lira’s consumption reports, access the Lira menu and the Report submenu.

There you can filter by attendance protocol, date, user who accessed it, and the services of the attendance. It is mandatory to provide a date range to perform the search.
In this report, it is possible to check the messages in detail by clicking the 'View messages' button, as well as the token consumption.

Best Practices

We created this section to help you in preparing the training files for LIRA.

Suggestions for Optimizing AI Training Files

To ensure the AI works efficiently and makes the most of the information provided, it is essential to structure the content clearly, in detail, and with proper context. Below are practical recommendations to improve the format and organization of the file:

Separation of Business Rules and Information
Business rules, such as topic restrictions or specific guidelines, should be handled directly by the AI’s internal mechanisms. Including them in the training file can be redundant and even harmful to the model’s performance, as it may create conflicts or overload the AI with unnecessary information.

Recommendations:

• Avoid redundancies: Do not include business rules that are already managed internally by the AI.
• Focus on content: Prioritize information that enriches the AI’s knowledge, such as detailed explanations, practical examples, and relevant contexts.

Information Format
The use of explicit questions and answers can limit the AI’s ability to generate dynamic and contextualized responses. Instead, it is best to present information in a continuous and explanatory way, providing context and details that allow the AI to understand and apply knowledge flexibly.

Example:

❌ Limited Format:
"How do I register on the portal?"

✔️ Explanatory and Contextualized Format:
"To register on the portal, go to the homepage and click the 'Create Account' button. Then, fill in the required fields such as name, email, and password. A valid ZIP code must be provided to complete the registration. After confirming your email, you will have full access to the portal’s features."

Benefits:

• The AI can generate more complete responses adapted to different situations.
• The additional context helps avoid misunderstandings or generic answers.

Content Enrichment

The AI benefits significantly from detailed, well-structured, and contextualized information. The richer and more complete the content, the better the AI’s ability to provide accurate and useful responses.

Recommendations:

• Add practical examples: Explain concepts with real cases or hypothetical situations.
• Provide context: Explain the "why," not just the "how."
• Include technical details (when relevant): If the subject requires it, provide technical information, but always in a clear and accessible way.
• Use analogies and comparisons: This helps the AI relate concepts and improve understanding.

Example:

"The registration process on the portal is similar to creating a profile on social networks, where the user needs to provide basic information and confirm their identity via email."

Organization and Structuring

Organizing the content is fundamental to facilitate the AI’s assimilation of information. Use a logical and hierarchical structure, with clear divisions between topics and subtopics.

Recommendations:

• Use subtitles and bullet points: Divide the content into clear, well-defined sections.
• Maintain a logical hierarchy: Start with general concepts and move on to specific details.

Best Practices

To ensure the training file is effective and high quality, follow these best practices:

✅ Clarity and Consistency:
Avoid ambiguous or contradictory information.
Use simple and direct language, avoiding excessive jargon.

✅ Avoid Redundancies:
Do not repeat content unnecessarily, but ensure all essential information is included.

✅ Regular Updates:
Review the file periodically to keep information updated and relevant.
Remove obsolete or outdated data.

✅ Relevant Context:
Prioritize useful and practical information, avoiding overly technical or irrelevant content.
Include real examples and applications to enrich the context.

✅ Testing and Validation:

• After reworking the file, validate the AI’s performance to ensure the information is being properly assimilated.
• Adjust the content based on the results obtained.

Next Steps

• If possible, we recommend reviewing the file based on these suggestions. If you need support in the reworking process or have questions, we are available to help.
• Below, we suggest an action plan to implement the improvements:

Analysis of Current Content:

• Identify improvement points in the current file.
• Remove redundancies and unnecessary information.

Content Restructuring:

• Organize the material into clear and well-defined sections.
• Add examples, contexts, and relevant details.

Validation and Testing:

• Test the AI’s performance with the new file.
• Adjust the content based on the results.

Continuous Updating:

• Establish a schedule for periodic reviews.
• Keep the file always updated and relevant.

With these improvements, the AI training file will be more complete, organized, and efficient, ensuring better performance and more precise, contextualized responses.