Discover the full potential of Diabolocom's AI

Artificial intelligence (AI) can be defined as a machine’s ability to simulate human cognitive processes, such as problem-solving, learning, perception, and decision-making. AI technology relies on machine learning, natural language processing, and artificial neural networks to achieve these goals.

There are currently two main types of artificial intelligence:

• Weak AI: This type of AI specializes in specific, limited tasks. AI uses Machine Learning (ML) algorithms to analyze data and generate recommendations.
• Strong AI: Unlike its predecessor, strong AI can understand and execute a wider range of complex tasks, similar to human thought.

Besides these two types of AI, there’s a hypothetical concept known as General Artificial Intelligence. This would encompass AI that surpasses human intelligence in all areas but raises a number of questions.

Today, AI is based on the following key stages: data collection, data processing, machine learning, and continuous improvement.

• Data collection: AI requires a large amount of data to learn. This data can be structured (such as tables) or unstructured (such as images or text).
• Data processing: Raw data are cleaned and prepared for use by machine learning algorithms.
• Machine learning: Algorithms enable the system to detect patterns and make decisions based on training data. Training data is used to teach and train AI models to recognize patterns and make decisions based on examples.
• Evaluation and improvement: Once the AI has been trained, it is evaluated using test data to measure its accuracy and performance. Models are then improved by adjusting specific parameters.

Thanks to these mechanisms, Artificial Intelligence can be deployed in various applications, such as contact center solutions, according to its capabilities and the tasks to be accomplished.

Automatic transcription is the process of converting a recorded phone call or voice message into text. Transcription can be time-consuming and tedious for contact centre agents. Moreover, manual transcriptions are only sometimes reliable.

Artificial intelligence, conversely, enables high value-added automatic transcription, thanks to the TLN system known as Natural Language Processing (NLP). NLP technologies interpret what the customer says or writes, often in a literal sense. With NLU (Natural Language Understanding), a sub-component of NLP, the system identifies the intentions and meaning behind the customer’s words or message.

An AI-enabled transcription solution like Diabolocom’s makes it possible to :

• Customer Interaction Analysis: Using NLP to convert audio recordings of calls into written text. This facilitates the analysis and review of interactions between agents and customers. In this way, you can assess service quality, identify recurring problems, and find areas for improvement.
• Semantic Understanding: With NLU, automatic transcription goes beyond literal interpretation, capturing the overall meaning of conversations. This helps detect your customers’ intentions and emotions.

The benefits of such transcription are manifold for the overall improvement of a contact centre, from agent performance to the final customer experience.

• For the Agent: Automatic transcriptions can ensure agents adhere to speech guidelines and compliance policies. Additionally, they can serve as examples during agent training, helping new recruits develop best practices.
• For the Customer: Analyzing automated transcripts helps identify recurring problems or points of dissatisfaction, allowing agents to anticipate needs and handle conflicts more effectively.

In short, automatic transcription integrating Natural Language Understanding  brings significant value to call centres. It improves operational efficiency and offers new insights into your business and customer needs.

Using artificial intelligence to synthesize texts provides numerous advantages for contact centres. AI enables key information to be extracted and rephrased quickly and easily.

Thanks to natural language processing (NLP), AI can produce two types of summaries: extractive and abstractive. The summary produced can be limited in terms of sentences or words, according to the parameters that the user has predefined in the tool.

Extractive summarization selects sentences representing the most relevant information from the original content. This technology identifies and extracts the essential sentences from the original unstructured text. In concrete terms, when you request a three-sentence summary, the tool returns three sentences from the document which are the best evaluated, i.e. which best reflect the general idea of the text.

Meanwhile, abstractive summarization captures the main idea without necessarily using the same words as the original text, a feat achieved through Generative Artificial Intelligence. Abstractive summarization generates a shortened text that doesn’t necessarily contain the same words as the original text, but captures the main idea. The resulting summary may differ in terms of vocabulary, but presents all the important ideas.

By combining the extracted information, AI produces augmented summaries that provide a global view of customer interactions, and enable you to detect potential malfunctions in the contact center.

A voicebot is a conversational robot that interacts using voice and natural language. While callbots automate incoming phone calls, voicebots specialize in vocal exchanges and communication. Using speech recognition and synthesis technology, it provides quick, consistent responses.

It’s available 24/7, providing continuous support while allowing human agents to focus on tasks of higher value. The voicebot records and analyzes interactions, gathering valuable data on customer preferences and trends.

When equipped with machine learning, it continually improves, tailoring responses based on customer feedback.