The goals include automating thousands of simultaneous phone calls, providing operator assistance during conversations, and utilizing emotional analysis to improve conversion rates. 

“We were looking for a partner with a proven track record in developing, training and applying AI models in business. AAI Labs proved to be a good fit because the projects they have completed validate the skills needed to develop our product. This solution will help to automate thousands of calls, improve customer engagement through real-time insights, and enhance their conversion rates and business outcomes,” said Stanislovas Globis, CEO of Export Discovery.

Objectives

Automated callerThe main capability of the system is making thousands of phone calls in English, Spanish, and German languages, analyzing the context of the conversation, and responding accurately, with real-time emotional and context-based adjustments.‍

Semi-automatic operator assistantThis AI assistant offers real-time suggestions and arguments to call operators, improving the quality of their responses and helping them to conclude sales more efficiently.

Emotional and voice analysisIn addition, AI-powered emotional analysis enhances the performance of  models by analyzing voice tones and emotional cues to optimize the conversation flow.

Implementation stages

1. Initial research and planning

The first stage involves  thorough technology research to evaluate AI models and tools, such as LLaMA for language processing, Whisper, Wav2vec 2.0 for speech recognition, and reinforcement learning algorithms for emotional analysis.Extensive data collection is conducted, gathering call transcripts, customer interactions, and emotional cues to effectively train the AI models.

2. AI model development and training

The next phase involves selecting the appropriate AI algorithms to handle real-time communication, emotional analysis, and decision-making support. Once the models are chosen, they are trained using the collected data, refining their ability to understand and respond to different languages, accents, and emotional cues, while also providing recommendations for call operators. Iterative testing and simulations are then conducted to improve the model accuracy and performance.

3. Integration with business tools and platforms

The third step is the system’s integration with the CRM platform to ensure seamless communication management. Following this, a pilot deployment has to be conducted in a controlled environment with a selected group of operators or businesses, allowing the system's ability to handle live customer interactions and automate phone calls to be closely monitored. Following successful pilot testing, the system can be expanded across B2B communications, allowing for the automation of thousands of calls simultaneously.

5. Continuous improvement and monitoring

Lastly, a feedback loop has to be established to continuously monitor system performance, gather user feedback, and analyze real-time data.  Afterwards, the system is gradually scaled to handle larger volumes of interactions, with infrastructure upgrades and optimizations ensuring scalability.‍

Expected results and impact