Overview
As the global power industry expands to meet consumer demands for electricity, the volume of data it produces has increased and become more complex. To process, analyze, and leverage these data streams, system managers rely on artificial intelligence (AI) and machine learning (ML) to quickly and efficiently compile the information needed to maintain safe operations.
We support a wide variety of AI & ML applications used by nuclear and transmission & distribution industries to help efficiently leverage large volumes of data, obtain insight and information quickly, as well as automate manually intensive and error-prone processes.
Our dedicated AI & ML team has successfully implemented machine learning across several projects in highly regulated environments under our existing quality procedures.
Why Us?
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Nuclear and T&D Experience
Our technical expertise in the Nuclear and T&D industries as well as our familiarity with station and system components and processes, allow us to leverage our engineering experience to influence Machine Learning models. This provides increased confidence in model performance and accuracy.
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Industry Leaders in Data Analytics
With many years of experience in data analytics, and as a recognized leader in AI/ML, our understanding of the first principles behind the methodologies allows us to tailor and modify our models to the specific needs of customers.
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Efficient Analysis
We can use AI and ML tools to efficiently analyze large volumes of data, using processes not possible with traditional data analysis. The data we analyze can be structured (i.e., databases) or unstructured (i.e., reports).
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Automation & Risk Reduction
Given a set of conditions, we can help you teach a machine to learn from previous human responses. This enables the capture of human expertise to manually automate intensive and error-prone processes, while allowing for better utilization of your workforce.
Technical Abilities
Our fast, efficient, and accurate AI & machine learning services help automate tasks, provide recommendations, find anomalies, and gain new insights within complex systems.
- Deep neural networks for prediction and classification tasks for tabular, images, time series, and text data.
- Advance neural network structure design and development, like stacked neural networks and ensemble learning techniques.
- Robust anomaly detection for different data types including time series, images, and text.
- Online and reinforcement learning development.
- Conventional and non-conventional transfer learning.
- Novel mathematical approaches for building state-of-the-art physics informed deep neural networks that incorporate underlying physical relations and subject matter expertise.
- Explaining and controlling AI predictions.
- Real-time computer vision models for object detection, instance segmentation, edge detection, and depth extraction tasks.
- Transfer-learning (non-conventional) using data augmentation on architectures like YOLO and masked R-CNN.
- Using principal component analysis, non-negative matrix factorization, and clustering to reveal unknown structures and patterns within data.
- Vector embedding to improve search capabilities.
- Creating models to generate new data including the use of Large Language Models (LLMs).
- Developing multi agents’ bots, retuning of opensource LLMs (domain specific).
- Informed augmentation of data into new representation.
- Development of multimodal AI including neural networks and chatbots to maximize insight extraction.
- Design and develop adversarial networks, and training discriminators (GANs).
- Adversarial Attack Detection and adversarial robustness.
- Transformers with attention and stable diffusion.
- Generation of 3D data from 2D representation.
- Automation of inspection for different data types.
- Automated report generation.
- Monitoring and diagnostics and rare event detection.
- Laser scanning and 3D modelling for digital twins.
- Digital models of plant components for optimization studies.
- Building virtual robots to complete tasks.
- Developing of AI bots and agents.
- Design and development of multi agent models to increase accuracy and performance.
- Custom tooling for reactor component inspection/maintenance.
- Integration of custom automation with robotic platforms such as SPOT from Boston Dynamics.
- High performance computing (HPC) and parallel programing for efficient calculation.
- GPU utilization.
- Agile management and utilizing Scrum and Kanban techniques.
- Version Control and change management strategies.
- Microsoft Azure (full suite including OpenAI and cognitive services), AWS and Google cloud.
- Extensive experience developing software in accordance with nuclear codes and standards (such as CSA N286.7).
- Software documentation including verification, validation, and qualification plans/reports.
- Data/AI governance and ethical use of AI.
Our Proven Experience
Quality Assurance & Technical Standards
- CIQB Standards
- CANDU Inspection Qualification Bureau
- CSA N286.0
- Management system requirements for nuclear facilities