Previously, we have explored various aspects of the ways data science and machine learning intertwine with natural events – from weather prediction to the impact of climate change on extreme phenomena and measuring the impact of disaster relief. AiDash, however, is aiming at something different: helping utility and energy companies, as well as governments and cities, manage the impact of natural disasters, including storms and wildfires. We connected with AiDash co-founder and CEO Abhishek Singh to learn more about its mission and approach, as well its newly released Disaster and Disruption Management System (DDMS).
Domain-specific AI
Singh describes himself as a serial entrepreneur with multiple successful exits. Hailing from India, Singh founded one of the world’s first mobile app development companies in 2005 and then an education tech company in 2011. Following the merger of Singh’s mobile tech company with a system integrator, the company was publicly listed, and Singh moved to the US. Eventually, he realized that power outages are a problem in the US, with the wildfires of 2017 were a turning point for him. That, and the fact that satellite technology has been maturing – with Singh marking 2018 as an inflection point for the technology – led to founding AiDash in 2020. AiDash notes that satellite technology has reached maturity as a viable tool. Over 1,000 satellites are launched every year, employing various electromagnetic bands, including multispectral bands and synthetic aperture radar (SAR) bands. The company uses satellite data, combined with a multitude of other data, and builds products around predictive AI models to allow preparation and resource placement, evaluate damages to understand what restoration is needed and which sites are accessible and help plan the restoration itself. AiDash uses a variety of data sources. Weather data, to be able to predict the course storms take and their intensity. Third-party or enterprise data, to know what assets need to be protected and what their locations are. Also: The EU AI Act could help get to Trustworthy AI, according to the Mozilla Foundation The company’s primary client thus far has been utility companies. For them, a common scenario involves damages caused by falling trees or floods. Vegetation, in general, is a key factor in AiDash AI models but not the only one. As Singh noted, AiDash has developed various AI models for specific use cases. Some of them include an encroachment model, an asset health model, a tree health model and an outage prediction model. Those models have taken considerable expertise to develop. As Singh noted, in order to do that, AiDash is employing people such as agronomists and pipeline integrity experts. “This is what differentiates a product from a technology solution. AI is good but not good enough if it’s not domain-specific, so the domain becomes very important. We have this team in-house, and their knowledge has been used in building these products and, more importantly, identifying what variables are more important than others”, said Singh.
Tree knowledge
To exemplify the application of domain knowledge, Singh referred to trees. As he explained, more than 50% of outages that happen during a storm are because of falling trees. Poles don’t normally fall on their own – generally, it’s trees that fall on wires and snap them or cause poles to fall. Therefore, he added that understanding trees is more important than understanding the weather in this context. “There are many weather companies. In fact, we partner with them – we don’t compete with them. We take their weather data, and we believe that the weather prediction model, which is also a complicated model, works. But then we supplement that with tree knowledge”, said Singh. In addition, AiDash uses data and models about the assets utilities manage. Things such as what parts may break when lightning strikes, or when devices were last serviced. This localized, domain-specific information is what makes predictions granular. How granular? Also: Averting the food crisis and restoring environmental balance with data-driven regenerative agriculture “We know each and every tree in the network. We know each and every asset in the network. We know their maintenance history. We know the health of the tree. Now, we can make predictions when we supplement that with weather information and the storm’s path in real-time. We don’t make a prediction that Texas will see this much damage. We make a prediction that this street in this city will see this much damage,” Singh said. In addition to utilizing domain knowledge and a wide array of data, Singh also identified something else as key to AiDash’s success: serving the right amount of information to the right people the right way. All the data live and feed the elaborate models under the hood and are only exposed when needed – for example if required by regulation. For the most part, what AiDash serves is solutions, not insights, as Singh put it. Users access DDMS via a mobile application and a web application. Mobile applications are meant to be used by people in the field, and they also serve to provide validation for the system’s predictions. For the people doing the planning, a web dashboard is provided, which they can use to see the status in real-time. Also: H2O.ai brings AI grandmaster-powered NLP to the enterprise DDMS is the latest addition to AiDash’s product suite, including the Intelligent Vegetation Management System, the Intelligent Sustainability Management System, the Asset Cockpit and Remote Monitoring & Inspection. DDMS is currently focused on storms and wildfires, with the goal being to extend it to other natural calamities like earthquakes and floods, Singh said. The company’s plans also include extending its customer base to public authorities. As Singh said, when data for a certain region are available, they can be used to deliver solutions to different entities. Some of these could also be given free of charge to government entities, especially in a disaster scenario, as AiDash does not incur an incremental cost. AiDash is headquartered in California, with its 215 employees spread in offices in San Jose and Austin in Texas, Washington DC, London and India. The company also has clients worldwide and has been seeing significant growth. As Singh shared, the goal is to go public around 2025.
