Web Based AI Calling Sales Agent Using Google NLP with a Web Interface

### 1. Overview The project aims to develop an AI phone calling bot system that integrates with Twilio for telephony services and Google Natural Language Processing (NLP) for understanding and responding to voice interactions. The system will include a web interface to upload lead spreadsheets, manage calls, and view call data and responses in real time through Google Sheets. Additionally, the bot will be capable of sending follow-up emails and making closing sales calls. ### 2. Objectives - Develop an AI bot capable of making phone calls using Twilio. - Utilize Google NLP to understand and respond to lead interactions. - Create a web interface for managing lead spreadsheets and tracking call data. - Update call responses in real-time on Google Sheets. - Automate follow-up email sending and closing sales calls. ### 3. System Components #### 3.1 Twilio Integration - **Phone Numbers**: Provision and manage phone numbers using Twilio. - **Call Handling**: Initiate, manage, and terminate calls using Twilio’s API. - **Call Recording**: Record calls for quality and training purposes. - **Real-Time Transcription**: Use Twilio’s capabilities for real-time call transcription to feed into the NLP system. #### 3.2 Google NLP Integration - **Speech-to-Text**: Convert spoken responses to text using Google’s Speech-to-Text API. - **Text Analysis**: Analyze the transcribed text to understand intent and respond appropriately using Google NLP. - **Entity Recognition**: Identify and extract key information from conversations for qualifying leads. #### 3.3 Web Interface - **User Authentication**: Secure login for users to manage their accounts and lead data. - **Lead Management**: Upload and manage lead spreadsheets (CSV format). - **Dashboard**: View call data, statuses, and performance metrics in real-time. - **Real-Time Updates**: Display real-time updates of call responses on Google Sheets. - **Call Controls**: Options to start, pause, and stop call campaigns. #### 3.4 Google Sheets Integration - **Data Sync**: Sync lead data and call responses between the web interface and Google Sheets. - **Real-Time Updates**: Display call data and responses in real-time on Google Sheets for monitoring. - **Response Logging**: Log call outcomes and responses for each lead. #### 3.5 Email and Follow-Up Automation - **Email Templates**: Predefined email templates for follow-up communication. - **Automated Email Sending**: Send follow-up emails automatically after calls based on predefined triggers. - **Follow-Up Calls**: Schedule and execute follow-up calls to leads who have shown interest. ### 4. Functional Requirements #### 4.1 User Roles - **Admin**: Full access to all features, including user management, lead management, and system configuration. - **Sales Agent**: Access to lead management, call handling, and real-time data monitoring. #### 4.2 Lead Management - **Upload Leads**: Upload lead data via CSV files. - **Edit Leads**: Add, edit, or delete lead information. - **Lead Status**: Track the status of each lead (e.g., Not Contacted, Contacted, Qualified, Disqualified). #### 4.3 Call Management - **Initiate Calls**: Start call campaigns with selected leads. - **Call Scripts**: Use predefined call scripts to guide the bot during interactions. - **Pause/Resume Calls**: Option to pause and resume call campaigns. #### 4.4 Real-Time Data Monitoring - **Google Sheets Integration**: Seamless integration to update call responses in real-time. - **Dashboard**: Real-time display of call metrics, lead statuses, and response summaries. #### 4.5 Follow-Up Automation - **Email Configuration**: Set up email accounts and templates for follow-up communication. - **Automated Emails**: Trigger automated emails based on call outcomes. - **Scheduled Follow-Up Calls**: Automate scheduling of follow-up calls for interested leads. ### 5. Technical Requirements #### 5.1 Infrastructure - **Cloud Hosting**: Host the web interface and backend services on a reliable cloud platform (e.g., AWS, Google Cloud, Azure). - **Database**: Use a scalable database to store lead information, call data, and user accounts. #### 5.2 Security - **Data Encryption**: Encrypt sensitive data in transit and at rest. - **User Authentication**: Implement secure authentication mechanisms, such as OAuth. - **Access Control**: Role-based access control to restrict access to sensitive features and data. ### 6. Implementation Plan #### 6.1 Phase 1: Requirements Gathering - Gather detailed requirements from stakeholders. - Define user roles and permissions. #### 6.2 Phase 2: Design - Design the system architecture. - Create wireframes and user interface designs. #### 6.3 Phase 3: Development - Develop the web interface and backend services. - Integrate Twilio for telephony services. - Implement Google NLP for voice interaction handling. - Develop real-time data syncing with Google Sheets. - Set up email and follow-up automation. #### 6.4 Phase 4: Testing - Conduct unit, integration, and user acceptance testing. - Test call handling and response accuracy. - Validate real-time data updates and follow-up automation. #### 6.5 Phase 5: Deployment - Deploy the system to the production environment. - Perform post-deployment testing and validation. #### 6.6 Phase 6: Training and Support - Provide training to users on system usage. - Offer ongoing support and maintenance. ### 7. Conclusion This scope outlines the development of a comprehensive AI phone calling bot system leveraging Twilio and Google NLP, with a web interface for real-time lead management and interaction tracking. The system will automate follow-up communications and streamline the lead qualification process, enhancing sales efficiency and effectiveness. For Reference please look at Bland AI, it is not reliable enough and support is non existent. This is how we envisage it to work, with enabling connection to google docs, allocate a Twilio number and the Bot Starts calling at set times, it will retry 1 hour later if it cannot reach a contact etc. It needs to be functional.