Automated customer support

The training of AI starts with pouring a large database, which can be:

Frequently Asked Questions (FAQs) - Introduction of pre-prepared questions and answers

Documentation and Manuals - Using corporate manuals, technical documentation, and other resources.

Historical data - Gathering real customer questions and answers from chats, emails, social media, and CRM systems.

This is a critical stage because the quality of the data determines how effectively AI will respond to customer queries.

Our AI analyzes incoming inquiries from various channels such as chats, emails, social media. The system recognizes not only keywords, but also the context and intentions of the user through natural language processing (NLP).

Furthermore, AI uses Sentiment Analysis to understand whether the customer is satisfied, disappointed, or angry and adapt the tone of the response.

Using NLP and machine learning (ML), AI generates natural-sounding responses and engages in dialogues with customers. In cases where the query requires more complex assistance or the customer prefers human interaction, AI automatically directs the conversation to an employee. This can happen in the following scenarios:

• Lack of relevant information in the database.
• Complex or specific customer cases.
• Clearly expressed desire from the customer to speak with a person.

Using Natural Language Processing (NLP) and Machine Learning (ML), AI generates natural-sounding responses to FAQs and engages in dialogues with customers. In cases where the query requires more complex assistance or the customer prefers human interaction, AI automatically directs the conversation to an employee. This can happen, for example, in the following situations:

• Lack of relevant information in the database.
• Complex or specific customer cases.
• Clearly expressed desire from the customer to speak with a person.

The AI constantly self-educates through analyzing previous conversations and feedback from clients. This education can be:

Active: Human operators are adding new questions and answers.

Passive: AI analyzes successful and unsuccessful conversations and improves its models automatically.

Reinforcement Learning: If a customer rates a response as helpful or unhelpful, the system adjusts its recommendations based on these ratings.

If the user does not find an exact answer in the FAQ, they can add the query to the database to be trained by an employee.

AI can provide seamless communication across multiple channels, including chatbots, social media, emails. Additionally, it remembers previous customer interactions, meaning that if the conversation starts in Facebook Messenger and then continues in an email, the customer won't have to repeat their information.

This ensures consistency and convenience in communication, regardless of the channel used by the client.

Example: The customer asks the voice assistant about the store's working hours, and AI immediately provides the correct answer