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

• Develop a complex solution for taxi aggregator service
• Analyze user feedback in various languages
• Implement a multilingual pipeline
• Establish quality control measures
• Create custom-tailored categories

Challenges

Efficiently analyzing users’ feedback in various languages. The need for a multilingual pipeline, quality control measures, and the requirement for custom-tailored categories.

Solutions

• Developed a complex solution for analyzing user feedback
• Created a multilingual pipeline for processing reviews
• Implemented quality control measures
• Established custom-tailored categories for feedback analysis

Outcomes

• Our comprehensive consulting solution resulted in a 20% decrease in the amount of negative feedback (1-2 stars)

Technologies Used

Tasks

• Enhancing AI models for improved efficiency.
• Transitioning systems to AWS for scalability.
• Consolidating data from various sources.
• Aligning releases with client design and usability needs

Challenges

The main challenge was migrating to AWS managed services to enhance performance and scalability, consolidating data sources, and integrating client-specific design and usability needs.

Solutions

• Migrated Adthena’s system to AWS, boosting scalability and performance.
• Achieved 20%+ cost savings through AI optimizations.
• Created complex reporting by aggregating data from Google, third parties, and Google Ads.
• Focused on user-centric design to exceed client expectations.
• Used Agile/Scrum for seamless collaboration and smooth execution.

Outcomes

• AWS migration improved scalability and performance, handling higher loads efficiently.
• AI optimizations reduced costs by over 20%.
• Design updates aligned releases with user needs, enhancing experience.
• Data consolidation and system upgrades boosted service efficiency for Adthena.

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

Tasks

• Analyze customer behavior
• Identify upselling and cross-selling opportunities
• Leverage data analytics to boost revenues
• Increase the average basket size
• Understand key customer segments
• Retain customers in a competitive market

Challenges

Solutions

• Developed a new recommender engine to grow the average basket size
• Implemented a new churn retention strategy based on model’s predictions
• Leveraged data analytics to understand key customer segments and opportunities

Outcomes

• The new recommender engine allowed to grow the average basket size by 0.4 items
• The new churn retention strategy based on our model’s predictions allowed early detection of churning clients, which led to a decrease in churn from almost 20% to 15%

Technologies Used

Tasks

• Establish a single client-consultant touchpoint for client questionnaires.
• Enable advanced analytics for consultants.
• Generate HRPP, Salary reports, and Graded Pay Structure.

Challenges

Solutions

• Developed a secure, single-touchpoint service with dynamic dashboards and drill-down capabilities.
• Built an autonomous system with a modern, user-friendly interface.
• Used historical data for predictive analytics and personalized recommendations.

Outcomes

Improved customer satisfaction with a user-friendly service, enhanced consultants’ effectiveness, enabling greater project scalability, reduced consultants’ data tasks, freeing resources, and showcased EY’s commitment to modern solutions and brand value.

Technologies Used