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

• Develop AI algorithms with dashboards and web portal development
• Implement and optimize CV algorithms for complex industrial environment cases
• Data preparation and annotation for industrial use cases.

Challenges

The project involved developing multiple algorithms and solutions. Additionally, implementing and integrating web portals and dashboards with the platform required extensive data preparation and annotation.

Solutions

• Designed and optimized CV algorithms for complex industrial scenarios
• Integrated real-time location systems (RTLS) and other sensors with CV outputs
• Developed dashboards and web portals for seamless usability.

Outcomes

• Achieved design, implementation, and optimization of CV algorithms for complex environments
• Successfully developed and optimized pipelines for performance improvement
• Delivered minimal involvement in day-to-day tasks, thanks to local leadership and efficient solutions.

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

• 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