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By leveraging the full range of AI technologies, including automatic natural language processing (NLP), natural language generation (NLG), pattern recognition algorithms, and predictive algorithms, our teams work with you to ensure optimal service and user experience.

Design of a conversational telephone agent

Many public operators use the telephone as their primary means of communication with their clients. With a volume of 100,000 calls per year, handled by a team of advisors, this operator is facing several constraints such as limited availability of the service due to opening hours (9 am-5 pm), an insufficient number of calls picked up, and difficulty in managing peak activity. To improve caller satisfaction and gain in performance, this operator called on the teams to develop and test a conversational telephone agent (Voicebot), i.e., an automated relational device based on the artificial intelligence (AI) foundation.

  • Implementation of a speech recognition solution (Speech to Text) and construction of a learning database.

  • Develop an application solution capable of interacting with the telephony system and automating the responses given to callers. 

  • Deployment of an API and a Web interface for the conversational voice agent's administration, management, and performance monitoring.

  • Automatic processing of 25% of calls made by an operator (30k / year)

Building a facial recognition and automatic photo captioning tool

The press service of a public administration, which was in charge of annotating official photos taken during public events, called on our teams to build a facial recognition and automatic photo captioning application.

  • Deep learning models detect faces in various photos and accurately identify the face.

  • Automatic photo annotation GUI to verify the inference and enrich the reference database.

  • Manual annotation is avoided on tens of thousands of photos.

AI SOLUTIONS FOR Improving business processes and end-user service

Our publications

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Labeling text clusters with keywords

We propose to explore several keyword extraction techniques to label text clusters obtained after a Text Clustering or a Topic Modeling pipeline. This work is following our previous articles about Topic Modeling and Text Clustering (here and here).


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