Tasks

• Provide expert services in pharmaceutical R&D for developing deep learning models for drug discovery, including predictions of organic compound solubility and interactions with proteins and DNA.

Challenges

Solutions

• Specialized service combining ML expertise with chemistry and drug discovery.
• Development and deployment of deep learning models to predict organic compound solubility and interactions with proteins/DNA.
• Utilization of extensive datasets and advanced algorithms for accurate insights.
• Seamless integration with existing R&D workflows to ensure minimal disruption.
• Continuous support and model updates for adapting to new data and research needs.

Outcomes

• Achieve a 15-20% reduction in research time and costs, accelerating the drug discovery process and improving the success rate of potential drug candidates.

Technologies Used

Tasks

• Optimize data acquisition from multiple real-time sensors monitoring the aniline synthesis process.
• Develop a Deep Learning algorithm to detect reactor issues using multisensor data.

Challenges

Solutions

• A complex Neural Network model was developed to perform sensor data fusion and predict reactor hard stop.

Outcomes

• The developed Neural Network model introduced additional control capabilities and improved process visibility, resulting in a 40% reduction in reactor failures.

Technologies Used

Tasks

• Develop a classification model to help customers understand their spending habits.
• Create an adjustable ML model to profile credit and debit transactions.
• Build a clustering ensemble to define regular and irregular transactions.

Challenges

The main challenge was to create a competitive advantage by utilizing AI to distinguish the financial app from others and to grow expertise in advanced analytics and machine learning.

Solutions

• An adjustable ML model that profiles customer transactions by merchant, purchase type, and income.
• A clustering ensemble that categorizes each user’s transactions as regular or irregular.

Outcomes

• The ML saving advisor became one of the most used features of the app.
• The first version of the model allowed accurate labeling of regular and irregular transactions in 80% of cases.

Technologies Used

Tasks

• esign and optimize ML pipelines for manufacturing.
• Develop scalable cloud infrastructure for ML.
• Prepare, clean, and annotate data for quality training.
• Implement CI/CD pipelines for ML models.

Challenges

The main challenge was to create a robust, scalable ML pipeline to process large volumes of manufacturing data in real-time, ensuring reliability and efficiency to support AI-driven co-pilot solutions for operator assistance.

Solutions

• Built a complete ML pipeline for data processing, training, and deployment.
• Enhanced scalability and reduced costs with cloud resources.
• Developed data cleaning tools for high-quality datasets.
• Integrated CI/CD for seamless model updates.

Outcomes

• Implementing the ML pipeline boosted manufacturing efficiency and accuracy, while the nFlux Guide™ enhanced operators’ achievement, safety, and reduced stress, leading to greater productivity.

Technologies Used

Tasks

• Build a single Master Data system
• Data collection and aggregation
• Data enrichment and processing
• Develop a business intelligence system

Challenges

Working with a database containing over 150 GB of data and processing more than 10 million transactions.

Solutions

• Built a single Master Data system
• Implemented key functions including data collection and aggregation, as well as data enrichment and processing
• Developed a business intelligence system for data-driven decision-making

Outcomes

• Built a single Master Data system
• Key functions included data collection and aggregation, as well as data enrichment and processing
• Developed a business intelligence system for data-driven decision-making

Technologies Used