Artificial Intelligence

With our artificial intelligence service, we strive to transform data into actionable insights, enabling companies to make informed and predictive decisions. Using advanced machine learning technology, we help optimize processes, identify hidden patterns, and create personalized user experiences.
Get ready to discover a new level of efficiency and intelligence with our artificial intelligence service.

Data Ingestion BMS (ALIS)

• It simplifies the manual input of sensor/plant data into the system, utilizing machine learning and deep learning models.
• It features advanced functionalities such as image recognition and optical character recognition (OCR), enabling automated data input from sensor images or resources of interest, extracting relevant information for the BMS.

Tools and Technologies

CPG Retail Analytics

• Delivered actionable insights for major retailers like Edeka, Carrefour, Auchan, Delhaize, Intergamma, improving shopper campaign performance and driving client initiatives.
• Analyzed consumer data, identified trends, and presented digital and in-store campaign strategies.  
• Conducted statistical data analysis and segmentation, built Machine Learning models for customer churn prediction, and targeted customers with personalized marketing offer using data mining techniques, for increased sales and boosted profits.

Tools and Technologies

Tunnel Predictive Maintenance

• The project aims to meet Predictive Maintenance requirements for road tunnels, using machine learning framework .
• We utilized advanced machine learning models with SCADA system data, including sensor readings, energy usage, and equipment metrics.  
• The main objective is to predict component failures and establish a safety rating system for overall tunnel operations.
• Sensor fusion approach enhanced tunnel safety, ensuring optimal conditions and mitigating risks through real-time safety evaluation. 

Tools and Technologies

Sentiment Analysis

• Analyzing tweets reveals how people cope with the pandemic and its psychological effects.
• Aids authorities in predicting decision impacts on the affected population for proactive measures.
• Gauges individuals' mental well-being, aiding in devising effective lockdown strategies for future pandemics.

Tools and Technologies

Medical Image Segmentation

• Apply advanced computer vision to medical imaging, aiding precise anatomical structure segmentation for diagnosis.
• Employed deep learning with CNN or advanced architectures like U-Net, training on annotated medical images.
• Preprocessed images, utilized data augmentation, and, if available, transfer learning. Optimized model parameters for high accuracy and generalization. Evaluated with model parameters optimized for high precision and generalization.
• Validated on separate Data set.

Tools and Technologies

Face Recognition System

• Develop an advanced facial recognition system using deep learning for accurate photo identification.
• Implemented a model trained on diverse, labeled facial images with variations in pose, lighting, and expressions. Employed deep neural networks (CNN, FaceNet, DeepFace) for recognition.
• Preprocessed images, utilized data augmentation, and employed transfer learning when applicable.
• Optimized model parameters for high accuracy. Evaluated with metrics (accuracy, precision, recall), fine-tuned based on results.

Tools and Technologies

Customer Churn Prediction

• Apply machine learning classifiers to telecommunications customer behavior data to predict and identify customers at risk of churning, enabling targeted retention strategies.
• Employed machine learning techniques on a dataset containing usage patterns, customer interactions, and satisfaction scores. Trained classifiers to predict churn likelihood and understand complex relationships between customer attributes and churn.
• The goal was to create a predictive model for early identification of potential churners, facilitating proactive retention measures by telecommunications companies.

Tools and Technologies

Electricity Demand

This project aims to enhance the accuracy of electricity demand forecasting by leveraging Long Short-Term Memory (LSTM) in the analysis of time series data. Using LSTM, a type of recurrent neural network (RNN), allows for more effective modeling of temporal dependencies, ultimately improving the reliability of predictions in the context of electricity demand fluctuations over time.

Tools and Technologies

Energy-saving Optimization

The energy-intensive nature of Thermo-mechanical Pulping (TMP) and proposes an energy-saving refining optimization strategy. The approach involves integrating machine learning algorithms and heuristic optimization methods. The initial steps include developing models for refining specific energy consumption (RSEC) and pulp quality identification using Artificial Neural Networks. 

Tools and Technologies

FDM Strain Sensor Measurement

This project involves integrating machine learning techniques like fuzzy logic and CNN into a Fused Disposition Modeling (FDM) for precise strain sensor measurements in order to improve accuracy and fabrication time of the sensor.

Tools and Technologies

AI/Analytics

• Database reengineering.
• ETL generations to extract data from the existing database to the new one ensuring historical data preservation.
• Orchestration of the pipeline and data operations.
• Development of dashboards for user types (System Administrator, Data Space User).
• Implementation of analysis functionalities.

Tools and Technologies