-
What We Do
-
Feasibility AssessmentMinimize risk, maximize results, and set your organization up for success.
-
AI LabLeverage shared experience and collaboration to drive adoption and results.
-
Catalog of OpportunitiesEmbark on your AI adoption journey with confidence.
-
Generative AI WorkshopUncover innovative ways to engage with your data to shape your future.
-
-
Featured
Navigating Bias in AI with Open-Source Toolkits
-
Some Industries We Support
-
Energy and ResourcesDrive innovation and promote sustainability while gaining a competitive edge.
-
Financial ServicesBoost efficiency, reduce costs, and streamline processes.
-
Forestry and AgriculturePave the way for a productive, efficient, and greener future.
-
HealthRevolutionize care delivery, improve outcomes, and save lives.
-
ManufacturingStreamline production, eliminate costly downtime, and enhance quality control.
-
-
Featured
AltaML Secures Spot on AIFinTech100 for Consecutive Year
-
What We Do
-
Contact
-
Featured
Jurisage and CiteRight Finalize Merger, Become Jurisage Group Inc.
AI-Powered Solutions for Energy and Resources
Drive efficiency, optimize operations, enable smart decision-making, and promote sustainability
The AI Revolution in Energy and Resources Has Begun
Artificial intelligence (AI) is fueling unprecedented transformation within the energy and resources industry. Early adopters are seizing opportunities for growth and innovation while gaining a significant competitive edge. Harness the power of AI to unlock unparalleled efficiency and pave the way for a sustainable and resilient future.
The Positive Impact of AI Adoption in the Industry
Operational Efficiency
AI-powered solutions facilitate data-driven decisions by enabling real-time monitoring and automation, directly enhancing business intelligence optimization.
Risk Mitigation
Anticipate hazards and predict potential equipment failures before they happen, allowing for preventative and proactive maintenance to create a safer environment for your team.
Cost Savings
AI analyzes large volumes of data and pinpoints patterns and areas of inefficiency. This insight leads to predictive maintenance that minimizes operational downtime and reduces costs.
ESG Benefits
Adopting AI for ESG efforts limits external pressures of falling behind while enabling companies to lower operating costs and increase their ROI with improved sustainability practices.
Enhance Productivity
Automate and streamline manual and repetitive tasks, freeing up human resources for more complex and value-added activities. Analyze data in real-time to enable predictive maintenance, minimizing downtime and maximizing efficiency.
New Revenue Streams
Gain valuable insights from advanced data analytics to help identify untapped market opportunities, optimize pricing strategies, and improve customer targeting, resulting in increased sales and revenue.
Trusted by Energy and Resource Industry Leaders
AI Solutions: Driving Innovation and Sustainability
AI is transforming the energy and resources industry by unlocking advanced data analysis, optimized resource allocation, and predictive maintenance capabilities. Achieve operational efficiency, reduce costs, and improve sustainability practices, all while gaining insights needed to make data-driven decisions that fuel long-term growth and provide a competitive edge in this dynamic market. As a trusted leader with extensive industry experience, AltaML’s innovative solutions are tailor-made to address real-world challenges, enabling you to achieve operational efficiency, mitigate risks, and discover new sources of revenue. Explore our approach and start your AI adoption journey today.
Analyze real-time data from sensors and historical records to detect patterns, optimize energy consumption, and identify areas of inefficiency. By enabling predictive maintenance and optimizing asset utilization, AI helps reduce downtime, improve operational efficiency, and minimize energy waste, leading to cost savings and enhanced overall energy management.
Optimize the planning and deployment of renewable energy resources, analyzing real-time data for grid optimization and stability, forecasting renewable energy production, and optimizing energy storage utilization. By leveraging AI algorithms, Partners can effectively manage fluctuations in renewable energy supply, maximize renewable resource utilization, and make informed investment and operation decisions.
Leverage advanced data analytics and machine learning algorithms to continuously monitor sensor data and analyze historical records to identify patterns, anomalies, and potential equipment failures to optimize maintenance schedules, prevent unexpected downtime, and reduce maintenance costs. Improve asset performance, enhance operational efficiency, and ensure reliability and safety of critical equipment.
Process and interpret complex datasets to identify potential drilling locations, optimize exploration efforts, and predict reservoir characteristics. Make informed decisions about resource allocation, enhance drilling efficiency, and improve overall extraction yields, leading to cost savings and increased operational efficiency.
Analyze demand data, inventory levels, market conditions, and logistics to optimize transformation routes, inventory management, and procurement decisions to reduce costs and improve efficiency. Enhance forecasting accuracy, streamline operations, minimize inventory holding costs, and ensure timely delivery of resources to improve overall supply chain performance.
AI can support detecting and assessing potential environmental impacts and risks by analyzing data from various sources, including sensors, satellite imagery, and environmental databases. AI-powered systems can support emissions monitoring, water quality, and biodiversity, enabling timely detection of anomalies and environmental regulation compliance, minimizing environmental impact while contributing to sustainability practices.
Transforming Organizations with AI
Initially, our goal was to create a digital twin of the site to allow inspectors to inspect the facility from an office environment safely. This would enable them to identify, pinpoint, and share locations of concern with contractors and project teams to rectify. However, once we saw the richness of the data we were collecting and how AltaML could utilize it, we changed our focus to create a more comprehensive solution that included machine learning techniques without needing full human interaction. Thus, creating a solution that was more efficient and accurate.
Mark Franklin P.Tech (Eng)
Manager of Innovation
Kleinfelder
AltaML has been critical in helping us understand what the process is and helping us take an ML project from start to finish to create something that has an impact and can be integrated into our business.
Andrew Nippard
Manager, Innovation and Data
TransAlta
Through cross-functional discussions with AltaML, we see new perspectives emerge. We tackled work that we otherwise wouldn’t have and done so in a cost-effective way, resulting in learnings that we can apply to our own analytics development and delivery cycles internally.
Sam Hayes
Head of Advanced Analytics
Suncor
Initially, our goal was to create a digital twin of the site to allow inspectors to inspect the facility from an office environment safely. This would enable them to identify, pinpoint, and share locations of concern with contractors and project teams to rectify. However, once we saw the richness of the data we were collecting and how AltaML could utilize it, we changed our focus to create a more comprehensive solution that included machine learning techniques without needing full human interaction. Thus, creating a solution that was more efficient and accurate.
Mark Franklin P.Tech (Eng)
Manager of Innovation
Kleinfelder
AltaML has been critical in helping us understand what the process is and helping us take an ML project from start to finish to create something that has an impact and can be integrated into our business.
Andrew Nippard
Manager, Innovation and Data
TransAlta
Through cross-functional discussions with AltaML, we see new perspectives emerge. We tackled work that we otherwise wouldn’t have and done so in a cost-effective way, resulting in learnings that we can apply to our own analytics development and delivery cycles internally.
Sam Hayes
Head of Advanced Analytics
Suncor
