JERSEY CITY, N.J., Nov. 15, 2021 /PRNewswire/ — ElectrifAi, one of the world’s leading companies in practical artificial intelligence (Ai) and pre-built machine learning (ML) models, today announced availability of Computer Vision (CV) and Machine Learning as a Service (MLaaS) for the Oil, Gas and Energy industries at ADIPEC in Abu Dhabi. ElectrifAi will be exhibiting at Booth 13605.
More than ever, oil and gas companies need to leverage the power of Ai, ML and CV to drive operational and cost efficiencies. The challenge is how? Oil, Gas and Energy companies generate extraordinary amounts of data across the enterprise. But frequently, the value and power of that data are never fully realized due to a variety of reasons, including the inability to access siloed data, general lack of data engineering and data science talent, and an inability to connect ML and Ai to the practical needs of business units.
For over 15 years, ElectrifAi has been a leader in practical Ai, ML and CV helping enterprise and government customers globally to quickly turn their data into a strategic weapon to drive revenue growth as well as cost and risk reduction. We have one of the largest libraries of pre-structured ML models that has been built and battle tested over the past 15 years. We have also developed innovative CV models to drive workplace safety as well as to reduce costs. And now with our innovative MLaaS offering, ElectrifAi enables companies to quickly realize the benefits of Ai and ML. We do all the heavy lifting. Clients simply describe their business use case. We tell them what data is required. Clients then supply the data and ElectrifAi takes over from there by training, operating and deploying the models and delivering results quickly. It’s that simple.
The collection of CV use cases ElectrifAi has created goes beyond the normal range of CV abilities yet seen. We have many CV-based solutions that could be applicable in this industry. For example: workplace safety, critical infrastructure monitoring, methane detection, and equipment surveillance.
In the Oil and Gas industry, nothing is more important than safety. Human error in high-risk environments can cause accidents, loss of life, and production stoppages. CV can play a vital role in helping to prevent injuries, keep rigs and production facilities running smoothly by alerting operators to potentially dangerous situations and remediating issues before an accident occurs. Similarly, CV can also play an essential role in monitoring valve banks and other critical infrastructure for human error as well as wear and tear leading to equipment failure. In addition to losses from production stoppages, there are also ancillary costs that can be avoided such as litigation, regulatory fines, and increases in liability insurance coverage.
MLaaS increases the efficiency and convenience of machine learning. Clients can quickly get started with machine learning without the exhaustive efforts of installation processes or providing their own servers. Although most companies are actively exploring the possibilities of Ai and ML, many struggle with basic challenges around data availability and quality as well as being able to recruit and retain data engineers and data scientists. ElectrifAi’s new MLaaS offering addresses and solves these challenges. With ElectrifAi’s MLaaS, companies need little to no experience to realize the maximum business and operational benefits of Ai and ML. MLaaS deploys easily within any cloud environment or on the customer premise. ElectrifAi will also develop, operate and maintain the models on behalf of the client making the MLaaS offering faster, better, cheaper and substantially less risk for clients.
By using MLaaS, clients can achieve many benefits to improve their operations and capabilities. Some of those benefits include lower costs, as one model can be cheaper than the annual cost of a single data scientist; faster time-to-deployment and lower project risk, as the average deployment is between 8-12 weeks for MLaaS versus 8-12 months to build new ML models; and faster time-to-value with a high return on investment (ROI).
One of ElectrifAi’s highly successful MLaaS offerings is an ML-based spend and procurement analytics solution that has been trusted by some of the largest companies in the world. It is quick and easily deployed in a client data center or any cloud environment the client chooses. Average realized savings for our SpendAi product fall in the 2-4% range. For a company with $1B in annual indirect spend, this represents $20–$40M dollars in annual savings. Other popular use cases include demand forecasting, dynamic pricing, customer segmentation and customer engagement.
“We’re pleased to introduce our Computer Vision and Machine Learning as a Service offerings to the global energy industry. Every company can now easily achieve the benefits of computer vision and machine learning with a very high ROI. We are helping energy companies across the globe grow and become more competitive through data-driven business decisions.” – Edward Scott, CEO, ElectrifAi
ElectrifAi is a global leader in business-ready machine learning models. ElectrifAi’s mission is to help organizations change the way they work through machine learning: driving revenue uplift, cost reduction as well as profit and performance improvement. Founded in 2004, ElectrifAi boasts seasoned industry leadership, a global team of domain experts, and a proven record of transforming structured and unstructured data at scale. ElectrifAi has a large library of Ai-based products that reaches across business functions, data systems, and teams to drive superior results in record time. ElectrifAi has approximately 200 data scientists, software engineers and employees with a proven record of dealing with over 2,000 customer implementations, mostly for Fortune 500 companies. At the heart of ElectrifAi’s mission is a commitment to making Ai and machine learning more understandable, practical, and profitable for businesses and industries across the globe. ElectrifAi is a global company with offices in Miami, Jersey City, Shanghai and New Delhi.