Revolutionizing Cold Calling with Aeon Ai

Model Integration

The platform required seamless integration of various AI models to support functions like text analysis, sentiment analysis, lead scoring, and natural language understanding.

Model Integration: 

 NLP Models: Leveraged models like BERT and GPT-3 for text analysis, sentiment analysis, and generating conversational scripts and chatbot responses.

 NER Models: Integrated Named Entity Recognition models to identify and extract crucial information from conversations.

Data Processing

Effective data processing was vital for understanding and extracting relevant information from voice conversations, such as company names, contact details, and key terms.

Reinforcement Learning (RL) Models: 

Employed Reinforcement Learning Algorithms for optimizing campaign scheduling, meeting bookings, and reminders.

Utilized Machine Learning Classification Models for lead scoring to target the most promising leads.

Conversation Flow

Crafting engaging and context-aware conversations that resonated with prospects was a challenge.

Voice Recognition and Generation Models:

Implemented Automatic Speech Recognition (ASR) models to transcribe voice calls into text for analysis.

Employed Text-to-Speech (TTS) models to convert text-based scripts into natural-sounding voices for outbound calls.

Voice Integration

Implementing voice recognition and generation models for transcribing voice calls and converting text-based scripts into natural-sounding voices was a complex task.

CRM Integration:

Utilized Recurrent Neural Networks (RNNs) to manage communication stages in the CRM system, enabling updates based on AI agent calls and analytics.

CRM Integration

The integration of CRM systems and the management of communication stages posed challenges in terms of data flow and adaptability.

Chatbot Development:

Developed Seq2Seq Models for building conversational chatbots that engaged with prospects, answered questions, and assisted with meeting scheduling.

Analytics and Insights

The platform required robust machine learning models to analyze transcripts, customer interactions, and derive actionable insights.

Data Analytics and Insights:

Applied Machine Learning for Analytics to analyze transcripts and customer interactions, providing data-driven insights for decision-making.

Voice over IP (VoIP) Integration:

Incorporated VoIP API and Integration to manage VoIP phone numbers, enabling calls directly from the platform.

 E-Signature Recognition:

Utilized Signature Detection and Recognition Models to detect and recognize e-signatures on contracts and agreements.

 Database Management:

Implemented AI-based query optimization techniques to efficiently retrieve and manage B2B and candidate information.