Tasks

• Develop machine learning models to analyze client behavior, segment users, and predict preferences.
• Build a personalized recommendation engine for workout plans, services, and offers.
• Create a scheduling optimization model to align client availability with trainer resources.
• Integrate a large language model as the conversational core of client’s in-app virtual assistant.
• Fine-tune the LLM to reflect the company’s brand tone, gym-specific procedures, and customer support guidelines.
• Build an ETL pipeline to unify data from client’s mobile app, gym systems, and CRM/ERP into a centralized repository.
• Implement secure cloud storage for models, logs, and backups using AWS S3.

Challenges

Solutions

• Client data used for modeling was anonymized using salted one-way hashes; personally identifiable information was excluded from analytics workflows.
• Custom instructions, prompts, and reinforcement feedback were used to shape the assistant’s responses, ensuring brand consistency and accuracy in gym-related queries.
• Built a robust data pipeline to extract, clean, and centralize information from multiple systems, improving data reliability for ML models.
• Combined behavior prediction, segmentation, and recommendations into one agent that could adapt based on real-time user feedback and platform usage patterns.
• The mobile app was updated with a clear privacy policy and tools for users to view, export, or delete their personal data.

Outcomes

• Successfully deployed a secure and compliant AI assistant that recommends content, manages schedules, and engages clients in personalized conversations.
• Improved user satisfaction through more relevant recommendations and faster response times.
• Maintained data privacy and built trust with clients through transparent handling of sensitive information.
• Laid a scalable foundation for ongoing machine learning improvements and LLM updates.

Technologies Used

Tasks

• Build a machine learning model to forecast price movements across selected trading instruments.
• Develop an AI agent that interprets model outputs to inform automated decision-making processes.
• Integrate reporting functionality to generate and deliver performance metrics and KPIs.
• Implement an external trigger system (e.g., email and webhook) to automatically activate the agent and handle follow-up responses.

Challenges

Solutions

• Developed adaptive models with online learning capabilities to regularly retrain on new data and adjust to changing market trends.
• Integrated macroeconomic indicators and real-time event data into the model to improve responsiveness to market shifts.
• Used automated feature engineering (AutoFE) tools to rapidly test and refine a wide range of input variables.
• Worked closely with financial experts to vet model features and interpret market behavior accurately.
• Deployed advanced NLP pipelines to extract signals from news headlines, social sentiment, and regulatory updates.
• Hosted models on high-performance infrastructure, optimizing serving latency and throughput using FastAPI.

Outcomes

• Delivered an AI agent capable of handling real-time price predictions for multiple instruments.
• Enabled automatic activation via email or webhook, with the agent retrieving inputs, executing analysis, and sending back KPI reports.
• Reduced manual intervention in forecasting and reporting workflows.
• Created a scalable foundation for automated decision-support tools in financial operations.

Technologies Used

Tasks

Challenges

Solutions

• Developed a Generative AI-powered platform with a ‘ChatGPT’ like interface.
• Enabled tailored responses to natural language queries of current IBC codes.
• Integrated key design parameters specified in ASCE 7-16 design standards.
• Incorporated IBC code tables and figures into tailored responses.
• Provided the ability to refine queries iteratively.
• Generated project-specific building code review reports.
• Ensured the platform is mobile and desktop ready.

Outcomes

• Transformed the way users access design requirements and insights.
• Improved efficiency in handling design compliance issues.
• Reduced project timeline and budget overruns caused by non-compliance.

Technologies Used

Tasks

• Implement AI/ML solutions to forecast material consumption and sales, optimizing procurement, supply chain, and production quantities in the Pharma industry.

Challenges

Solutions

• Implement ARIMA and LSTM models for time series forecasting of material usage and sales.
• Integrate models with ERP and CRM systems for seamless data flow.
• Establish a robust data governance framework.
• Enable continuous model retraining to adapt to market changes.
• Enhance procurement planning and production schedules to reduce material waste.

Outcomes

• Achieve a 10-15% reduction in material waste, improving overall efficiency and reducing costs.

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

• Develop a predictive model on the wind energy production forecast
• Analyze historical data for accuracy
• Implement and test the model

Challenges

Accurately forecast wind energy production based on historical data.

Solutions

• Developed a predictive model for wind energy production
• Utilized historical data for training and testing the model
• Implemented machine learning techniques to enhance forecast accuracy

Outcomes

• Model Forecast – mean Absolute Percentage Error was <10

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

• Enable real-time audio and text recognition
• Provide instant insights for sellers
• Develop a new feature for customizing video and audio content

Challenges

Enabling real-time audio and text recognition, providing instant insights for sellers. Developing a new feature for customizing video and audio content, aiming to enhance the platform’s functionality.

Solutions

• Released real-time audio and text recognition & processing to offer immediate information to sellers
• Developed a new feature of video and audio customization
• Data annotation for multilanguage audio data

Outcomes

• Immediate information delivery through real-time recognition
• Enhanced platform functionality with customizable video and audio content
• Multilanguage data annotation for broader reach

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