Retail Guardian AI

Empower your business: enhance service quality and drive revenue growth

ABOUT US

We have developed an alternative, efficient, and reliable software solution for monitoring service quality in physical stores that encompasses features offered by all competitors: it tracks both the sales process and employee performance. Clients do not incur expenses for equipment or employee salaries. With our software system, managing the entire network of sales points becomes possible from just one screen.

Our software consists of analytical modules hosted on remote servers. The complex of programs automatically monitors each retail point daily using artificial intelligence and Big Data analysis technologies. The software generates detailed reports and interprets the results obtained to enhance service quality, customer satisfaction, and the overall efficiency of the entire network.

What technologies do we use?

Computer vision: In our project, computer vision is used to analyze images and videos from surveillance cameras in retail points. This allows for the automatic detection and classification of various interaction scenarios between staff and customers, such as greetings, consultations, purchase processing, and farewells.

Object recognition: Object recognition technology is used to identify specific objects in images or videos. This allows for determining the key elements of interaction, such as products, staff, and customers, and analyzing their behavior.

Speech transcription: This technology is applied to automatically convert speech information from video or audio recordings into text. Speech transcription helps analyze dialogues between staff and customers, identify key words and phrases related to service quality.

Heatmaps: Heatmaps are used to visualize the intensity of interaction in different areas of the retail point. They identify zones of greatest customer interest, potential problem areas, and help optimize staff placement.

Recurrent Neural Networks (RNN): RNNs are used to analyze the sequence of actions of staff and customers in retail points. This allows for identifying behavior patterns, forecasting possible service issues, and providing recommendations for process improvement.

Cascade neural networks: they process information obtained from cameras and microphones step by step. For example, the first one recognizes the entire process from the moment a visitor appears to leaving the premises, the second one breaks down the material into time intervals, the third one analyzes the interaction between the customer and the seller, and so on. Such sequential analysis allows for accurately identifying not only problem areas in service but also successful working methods that can be scaled to increase business efficiency.