ZBrain meeting preparation agent automates the process of gathering and organizing relevant information for upcoming meetings. By utilizing a Large Language Model (LLM), the agent extracts and summarizes user information and analyzes prior communications to prepare detailed meeting preparation reports.

Challenges the Meeting Preparation Agent Addresses:

Preparing for meetings is often resource-intensive and inefficient. Team members must manually navigate cluttered inboxes and various platforms to gather necessary details and attendee backgrounds, a process requiring significant manual effort. Synthesizing past conversations adds complexity, particularly when information is dispersed across multiple communications. This inefficiency increases the risk of missing crucial details, causing preparation delays and potential miscommunication.

ZBrain meeting preparation agent simplifies the meeting research and preparation by automating information aggregation and organization. Leveraging AI to extract, synthesize, and organize data from diverse sources into actionable insights, this agent ensures comprehensive preparation. This automation minimizes the chance of overlooking important details and maximizes meeting efficiency, ensuring all participants are well-prepared and facilitating more effective and engaging interactions.

How the Agent Works

ZBrain meeting preparation agent is designed to automate the research required for meetings by gathering and organizing relevant information. Leveraging the power of an LLM, it summarizes the professional background and key information about the attendee and generates a comprehensive meeting research report. Below, we outline the detailed steps that showcase the agent's workflow, from the agent activation to attendee information retrieval and meeting report generation.

Step 1: Agent Activation and Initial Data Extraction

The agent is activated when a new email with event or meeting details is received in the designated inbox.

Key Tasks:

• Email Monitoring and Activation: The agent continuously monitors incoming emails and is triggered by specific keywords or calendar invite links that indicate an event-related email. This immediate detection initiates the activation process.
• Data Extraction from Email and Google Calendar: Upon activation, the agent extracts critical event details from the email content and any linked Google Calendar invites. It uses an LLM to extract details from Google Calendar, such as the meeting date, time, organizer, attendees, and other pertinent details like location and agenda.
• Formatting and Structuring Data: The agent formats the extracted data into a structured format, organizing names, email IDs, roles (organizer, attendees), and other relevant details. This structured data is then prepared for easy tracking and reference in subsequent processes.

Outcome:

• Data Readiness and Contextual Preparation: The agent ensures that all relevant information is efficiently extracted and organized. This comprehensive preparation allows for the smooth progression to more detailed planning and analytical tasks in the subsequent steps.

Step 2: Domain Name Analysis and Profile Search

The agent evaluates the domain name of each attendee's email address to ascertain if it is associated with a corporate domain or a common personal domain (e.g., Gmail, Yahoo). At this step, the agent uses an LLM to summarise the extracted details.

Key Tasks:

• Domain Classification: Identifies whether each email address is linked to a corporate or personal domain, guiding the subsequent search strategy.
• LinkedIn Profile Search: For email addresses from corporate domains, the agent conducts a LinkedIn search to collect professional profiles and gather insights about the attendees, such as designation and organizational connections.
• Google Search for Personal Domains: When personal email domains (e.g., Gmail, Yahoo) are detected, the agent performs a Google search to access publicly available information about the attendees, including their professional background and relevant activities.
• Profile Summarization: Utilizing a Large Language Model (LLM), the agent summarizes the collected information, highlighting key professional details like job title, company affiliation, recent activities, and relevant skills.

Outcome:

• Comprehensive Attendee Insights: This step generates detailed and summarized profiles of all attendees, enhancing the preparation for the meeting by providing a deeper understanding of each participant's professional background and current role. This information aids in tailoring discussions and ensuring productive and relevant interactions during the meeting.

Step 3: Previous Conversations Retrieval and Processing

The agent retrieves previous emails or message exchanges with the identified attendees to build context for the upcoming meeting.

Key Tasks:

• Message History Retrieval: Accesses the communication history with each attendee, pulling records of past interactions that pertain to the topic.
• Content Extraction: The agent loops through all previous messages, identifying important details like specific tasks, deadlines, or issues that need to be addressed. It extracts key details from each past communication, such as the dates of interactions, main topics discussed, and any pending action items or follow-up tasks.
• Data Compilation: Aggregates and organizes this extracted information in a structured format to facilitate easy access and analysis.

Outcome:

• Contextual Preparation for Meeting: This step ensures that all relevant historical interactions are considered in the meeting preparation, providing a comprehensive background that enhances the relevance and depth of the upcoming discussion. The organized data helps prevent overlooking critical past discussions and aligns current meeting objectives with historical insights.

Step 4: Meeting Report Generation

The agent uses an LLM to generate a comprehensive meeting report using extracted details such as meeting information, attendee profiles, and conversation history.

Key Tasks:

• Report Synthesis: The LLM synthesizes the information into a detailed meeting report. This report includes:
  • Meeting details such as time, date, attendees, and organizer.
  • An overview of the project, progress, ongoing tasks, and challenges.
  • Identification of next steps and action items.
• Integration of any feedback or suggestions gathered from previous communications.
• Document Storage: The meeting report is saved in a central location, such as a project management system or document management system, ensuring that it is accessible to all relevant stakeholders.

Outcome:

• Comprehensive Meeting Preparation Report: The generated meeting report serves as a thorough preparation guide for the upcoming meeting, ensuring that all relevant details are documented and easily accessible for effective discussion and decision-making.

Why use Meeting Preparation Agent?

• Enhanced Productivity: Automates tedious meeting preparation tasks, allowing users to focus on strategic activities rather than spending time gathering and organizing information.
• Error Reduction: Minimizes the risk of missing crucial details or misinterpreting information by using LLMs for data extraction, analysis, and summarization.
• Scalable and Adaptable: Easily scales to handle multiple meetings across different teams or projects and adapts to various organizational workflows and requirements.
• Contextual Insights: Leverages past communication history and attendee profiles to provide context for upcoming meetings, enabling continuity and effective decision-making.
• Time Savings: Reduces the time spent searching for attendee details, summarizing previous conversations, and compiling meeting agendas, significantly improving efficiency.

Utilities

Meeting Preparation Agent

Provides meeting preparation reports with details about external attendees, enhancing meeting effectiveness.