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In an interview with CIO Applications, Kumar shares his insights on how App Orchid builds apps that harness artificial intelligence and machine learning to make predictive analytics and risk management for utility companies as simple as asking a question on Google.
Can you give us an overview of your company?
Prior to App Orchid, as the founder and CEO of SpaceTime Insight, a company that was later acquired by Nokia, I was engrossed in solving business challenges in the utilities and energy space, and pioneered the idea of analytics applicable for IoT, distributed over massive geographies. App Orchid, founded in 2013, was established with a core focus of leveraging AI, machine learning, and natural language processing (NLP) to develop powerful applications that would eliminate the challenges of modern-day utility enterprises.
In the pursuit of solving the adversities, there is one challenge in particular that we wanted to address. Today’s electric grid is a myriad of smart meters, communication controls, power line sensors, and other intelligent endpoints. All these devices are creating an exponential growth in the amount of analytical data. However, these structured data sets only represent a fraction of the utility’s collective intelligence. The rest is trapped in unstructured data that can be found in field assessments, customer interactions, equipment manuals, and perhaps even more important, experiential observations from utility personnel at all levels that we refer to as “tribal knowledge.” What if we could get that untapped information out into the digital ecosystem? This became the eureka moment for App Orchid. Our ability to integrate IoT with our domain knowledge and leverage AI to craft solutions made us a game-changing solution provider in the utility space.
What are some of the pressing challenges in the utility industry?
Today, the advent of renewable energies like wind and solar power are bringing massive transformation to a century-old model of providing electricity. Nonetheless, there are still numerous challenges associated with implementing renewable sources of energy. One of the primary problems with renewable power sources is that they are very intermittent in nature; you can never predict if the velocity of the wind or the intensity of solar rays is going to remain consistent.
App Orchid builds apps that harness artificial intelligence and machine learning to make predictive analytics and risk management for utility companies as simple as asking a question on Google
Can you elaborate further on your applications portfolio?
In the power distribution realm, electrical energy needs to be transmitted over long distances through high-power transmission lines. When the energy leaves the power plant, the voltage is stepped up with the help of step-up transformers, and the voltage is again stepped down through step-down transformers at city sub-stations. Throughout this distribution network, there are numerous challenges involved that a utility company has to overcome. What App Orchid does is, we look at the entire energy management value chain and break its processes down into multiple applications. The distribution intelligence app, for instance, is able to tap into the data, analyze it, and provide insights that enable the energy company to make smarter decisions in terms of energy distribution practices.
How do you incorporate tribal knowledge within these applications?
Tribal knowledge is basically the way how human beings think. Human psychology tends to gravitate toward efficiency in performance and cost at the same time. For example, as a consumer of electricity, I would like to ensure comfort, but if I could save a few dollars every month by reducing the amount of air conditioning during the peak hours of the day, I would certainly utilize that option. Thus, trying to understand the tribal instincts of human beings is very different from understanding systemic, structured data. By leveraging AI and machine learning capabilities, we aim to understand this tribal knowledge (gut knowledge) and incorporate them into the traditional analytics landscape, making it more powerful.
Could you illustrate the robustness of your solutions through a case study?
One of the best examples would be how we utilize our AI-based applications to ensure the safety of utility workers. The utilities industry, by definition, involves very high-risk jobs. Engineers either have to inspect high-voltage transmission signals 200 feet above the ground or go 20 feet underground to fix a problem in hydro-electric power plants. We are constantly collecting information from various oil and gas, and mining companies to increase our knowledge base on how different accidents occur and then we are extending this knowledge to our clients so that even if their engineer wasn’t exposed to any adverse situation before, he could be prepared.
What are App Orchid’s future endeavors?
We are a young organization with tremendous scope for improvement. In the course of the next couple of years, we are looking to apply our utility sector’s knowledge into other industries such as insurance and healthcare. The world of insurance involves a lot of risk-taking and our knowledge of safety and asset failure can easily be applied for worker’s compensation, understanding, and estimating risk. Another very interesting area for us is healthcare. Though we are in the early stages, we can very easily capture telemetry data from devices like smartwatches, and provide deep insights. We are really excited about some great possibilities in collateral industries by implementing the solutions that we have initially designed for the utilities world.