ElectrifAi, one of the world’s leading companies in practical artificial intelligence (AI) and pre-built machine learning (ML) models, today announced expanded offerings of pre-built and pre-structured ML models for Amazon SageMaker, including models available from the Computer Vision and Image Analytics collections. Amazon SageMaker is a ML service from Amazon Web Services (AWS) that helps data scientists and developers to prepare, build, train and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML To learn more, we conducted an interview with ElectrifAi.
1. Can you tell us more about 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 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. 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 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 and previously headquartered in Jersey City, with offices located in Shanghai and New Delhi.
2. What kinds of services does ElectrifAi provide?
A large library of AI-based products reaching across business functions, data systems, and teams to drive superior results in record time. Readily available on Amazon SageMaker and Google Cloud Platform, ElectrifAi provides the world’s largest library of pre-built machine learning (ML) models that integrate seamlessly into existing workflows. With ElectrifAi’s comprehensive ML bank, clients can eliminate the time-consuming risk to hire an additional team to build new ML models; therefore, reducing cost and increasing time-to-value to bring solutions and important data faster.
3. What kinds of machine learning models does ElectrifAi integrate into existing workflows?
Nearly all of ElectrifAi’s ML models are geared toward integration into existing workflows. A few examples are for the Healthcare industry: 1) Revenue cycle management in terms of finding missing charges that were not added to a bill- but should be added; 2) A pre-trained lung segmentation model for CT scans which determines the presence of lungs and, if confirmed, it computes a segmentation of the lungs directly in a 3D process; and 3) Patient engagement and community outreach to bring patients back to the hospital for additional treatment.
4. Which industries can benefit from ElectrifAi?
Practically every industry out there. With ElectrifAi’s vast library of pre-built ML models, these models have universal uses across all industries. These industries and the benefits and information they receive include:
- Patient Engagement
- Build awareness with target patients to optimize care
- Contextually understand patients and their conditions to better direct care
- Address prescription adherence to positively impact health outcomes
- Demand Forecasting
- Targeting by identifying the most attractive prospects, the best channels for reaching them and the most optimal times to target them
- Continuously tailor and connect both inbound and outbound patient acquisition
- Smart Scheduling
- Leverage actionable insights for smart scheduling
- Tailor programming to customer lifecycle goals
- Identify optimal timing for structuring communication across marketing channels
- Generate significant cost saving opportunities from applying machine learning to combined spend and contract data
- Discover negotiated rebates hidden in contracts and compare to spend data
- Bridge the gap between what happened versus what should have happened
- Prioritizes your daily workload: Confirm and resolve missed charges that yield the highest returns at the most efficient rate
- User-friendly dashboards: Display top-performing auditors. Identify and adapt best practices to improve lower performing departments
- Robust payer contract analysis: Produces knowledge of unique payer relationships, trends and outcomes
- Automatic Data Visualization: Provides a comprehensive view of departments, auditor and hospital performances – for greater transparency
- Federal Government Organizations
- CPARS and RFP process
- Contract funds management
- Payment integrity and payment error versus fraud
- Fraud waste and abuse
- Program performance, program integrity and health of program
- Spend rate
- Reallocation of funds, vendor management and procurement
- Scope of work comparisons
- State Agencies
- State CIOs
- Innovate through predictive algorithms and machine learning (ML) without the expense of a platform that requires years of customization to realize value
- Increase data quality through data cleansing and aggregation, focusing on creating action and better outcomes
- Reduce project delivery timeframes with faster time to market for your agency customers and see the value of the data in 4 to 6 weeks, as opposed to 4 to 6 months
- Manage project management organization efficiency and effectiveness with better insight into resource availability and allocation, project timeline adherence along with both contract and contractor performance
- State Procurement
- Improve service to your inter-agency and county partners through better visibility and transparency
- Increase purchasing power by taking advantage of greater volume discounting
- Create transparency through aggregated data views and vendor consolidation
- Maximize adherence to contractual terms and get the discounts you have earned
- Increase reporting by identifying what should have happened and reconciling against what actually occurred
- State Pension Funds
- Enhance returns and reduce risk with actionable insights for better portfolio management
- Aggregate transparency to allocate capital efficiently
- Improve efficiency by focusing on alpha-generating activities and aggregate exposure and active positions
- State University Systems
- Increase graduation rates using machine learning in the screening of applications
- Reduce spend through consolidated views of expenditures
- Innovate with cutting-edge AI visualization teaching tools
- State university systems: Increase graduation rates using machine learning in the screening of applications
- State CIOs
- Federal Government Organizations
- Energy, Oil, and Gas
- Identify cost-saving opportunities to generate more value
- Improve operational transparency
- Redefine supplier relationships
- Enhance spending efficiency and mitigate supply chain risks
- Increase automation and boost workforce productivity
- Predict customer behavior and intent to improve personalization
- Improve customer retention
- Boost customer experience and guarantee fast response times
- Analyze data for unique and actionable insights
- SpendAi leverages the power of machine learning to identify more spend patterns and saving opportunities than other technologies can with greater accuracy
- ContractAi reads and understands contractual language and clauses that are all-too-often inaccessible—giving you deep and detailed visibility into your supplier relationships while uncovering hidden risk
- Customer Engagement Models
- Transform marketing and customer engagement by injecting machine learning insights that deliver more personalized, effective campaigns and customer experience
- Retail and Consumer Package Goods (CPG)
- Analyze huge data sets to increase revenue, streamline and automate purchasing and inventory control based on predicted customer behaviors
- Send targeted and personalized campaigns to your customers
- Assist the CPG industry stay relevant in a fast-changing and disruptive environment
- Identify changing patterns in data and output decisions to drive success and revenue
- Financial Services
- Identify operational efficiencies
- Mitigate risk with early fraud detection flags
- Improve critical compliance processes and automatically identify weaknesses
- Enhance informed and data-driven investment decisions aligned with desired portfolio characteristics
- Predict and mitigate customer churn
- Equip claims adjusters, underwriters and other knowledge workers
- Apply insights to augment underwriting models and processes
- Develop highly personalized services and marketing content
- Align premiums with forecasted risk
- Monitor potential exposure, risk and compliance breaches
5. What challenges can ElectrifAi help in solving amid the COVID-19 pandemic?
Consumer and business behavior have been impacted since the beginning of the pandemic, altering telecommuting, travel, meetings, shopping and especially healthcare. As a result, healthcare providers and drug companies need to find ways to find cost-savings and drive more efficiency. This can be accomplished by off-the-shelf artificial intelligence solutions, such as those provided by ElectrifAi. The company’s machine learning model library can help directly with process efficiency, productivity increases, building up resilience and contributing to significant cost savings.
6. How would your services increment the company’s annual revenue in addition to cutting its costs?
We have many machine learning models that are related to upsell, cross-sell, customer acquisition, customer retention, and other cost efficiency tools. Last year we added over $100M in incremental revenue uplift to a single customer alone just from upsell and cross-sell models. Our customer acquisition models have put people on cruises and students in universities, just to name two examples; these ML models drive tremendous return on investment.
7. Do you provide partnership opportunities? Can you elaborate on that?
Yes, absolutely. Our Global Alliances Program is developed for any business that wants to partner with ElectrifAi. Our program is designed to accelerate innovation and collaboration, while giving you access to the most comprehensive portfolio of AI and ML products in the enterprise industry. Our effective AI and ML delivers pre-built machine learning models and machine learning enabled software that can be applied across multiple industries. Together we accelerate and create unparalleled results, while empowering our partners to achieve higher client satisfaction and improved profits. ElectrifAi provides partners with sales, technical training and industry expertise to empower greater success.
8. What makes you unique amongst other competitors in industry?
There is no other machine learning provider like us. Most other enterprise AI companies provide platforms upon which machine learning solutions are built using their own data scientists and engineers. ElectrifAi already has more than 1,000 pre-built AI solutions leveraging domain specific KPIs, of which hundreds are applicable and specific to the healthcare industry. Why build, when you can buy? We provide solutions that can be deployed faster, with greater efficiency and more reliable results to save your company time and money. Most other AI providers require more time and capital investment with slower, if not inadequate, results. ElectrifAi values transparency and delivers models that are interpretable and explainable, allowing business to understand how decisions are being made.
9. Where do you see ElectrifAi in the next 5-years?
In the recent past, there was a trend of getting involved with AI for the sake of using AI. It was trendy. The market got very crowded with companies claiming to provide AI but there was no return on investment. In five years, I think the companies providing AI will start to thin out and their clients will turn back to solutions that provide heightened return on investment and solutions that solve business problems. You’ll see very few companies that do machine learning at scale. ElectrifAi will be one of only a few companies that could possibly compete in that space. Except for a few of the largest tech firms, ready-to-go machine learning models will be the primary way companies will effectively be using machine learning. ElectrifAi stands to be the dominant player in that space.
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