Designing a foolproof system takes more than what is usually required, a prediction mechanism that can prevent some of the annoying breakdowns. Digital twins, which are the data-driven representations of physical systems, offer virtual tools powered by IoT sensors and data analytics. They simulate the real-world environment so that the machine-level problems are predicted accurately with inputs from the machinery in real-time. Intangles, an AI company designs digital- twin solutions for the management, tracking, and maintenance of automobiles. Analytics Insight has engaged in an exclusive interview with Aman Singh, Co-founder, and Head of Analytics at Intangles.
1. Kindly brief us about the company, its specialization, and the services that your company offers.
Our system includes unique hardware that can communicate with the latest vehicle diagnostic protocols. This technology can gather data from a wide range of engines across globally prevalent commercial vehicle platforms. The captured data is sent to the cloud through cellular networks and consumed by a fully customized server backbone. The data is then channeled through a workflow of cascading machine learning algorithms, which mimic the behavior of physical systems in the virtual space, including systems like fuel injection, engine cooling, and battery charging, to name a few. By coupling power-train telemetry with geospatial intelligence, the algorithms provide predictive insights on component failure, detailed statistics on vehicle performance (fuel consumption, distance, run hours), driving behavior, and automated reporting. Powerful predictive models for critical engine functions enable alerts well before a breakdown occurs.
These notifications are coupled with appropriate instructions to mitigate significant losses. Such precise diagnostic instructions with a predictive insight are altering the mobility ecosystem. We recognize that the present suit of analytics in the telematics market is not able to interpret diverse signal streams and generate viable, actionable intelligence. That is why we have taken the novel approach of creating digital twins of specialized power-trained battery charging, engine cooling, fuel injection, and assisted air intake functions.
2. With what mission and objectives was the company set up? In short, tell us about your journey since the inception of the company.
Our passion for data sciences and automobile technologies led us to the exploration of On-Board Diagnostics data on passenger vehicles. It was fairly discernible that there was limited scope for predictive health diagnostics in the passenger vehicle segment for routine upkeep by a significant majority of individual vehicle owners. This led us to analyze data streams on commercial vehicles with heavy-duty diesel, including trucks and buses, which opened doors to a vast arena of opportunities. With a clear use case in sight, we developed a proprietary hardware interface capable of collecting data from CV (Commercial Vehicle) platforms across OEMs, fuel injection, and emissions technologies. This was augmented with a state-of-the-art edge-to-cloud communication backbone and a suite of proprietary algorithms targeted toward predictive health alerts, driver behavior profiling, fuel pilferage, and geospatial intelligence.
We were really lucky to acquire several early-stage clients that have helped us build our technology by allowing data integration across operational disciplines in cargo/long-haul logistics and passenger transport. When we had a population of 15K assets across the globe, large vehicle OEMs in the CV space began paying attention to us. In 2019, we onboarded our first billion-dollar OEM, with a production line integration spanning the complete medium, light, and heavy commercial vehicle range. This spawned new markets, such as machine learning for engine calibration and vehicle-integrated diagnostic monitors. More automakers followed suit. Today we have onboarded 7 OEMs across 11 countries where we are operational. Furthermore, the platform already has over 8,000 fleet operators. It now integrates 60K+ assets across cargo, haulage, last-mile mobility, EV, and passenger transport. We onboard around 600 fleet operators every month and process an astounding 4 billion sensory data points per day. We estimate 5x growth in FY’23, with some of the top brands in mobility already signed up as customers.
3. Brief us about the proactive Founder/CEO of the company and his/her contributions to the company and the industry.
Anup manages strategy, finance, HR, the company’s vision and alignment of resources, performance management, and accountability across the board. He co-founded Tavisca Solutions in 2008, a leading 300 people+ travel technology company, which was acquired in 2018 by Cx Loyalty, a ~ US$ 1 Bn loyalty and customer engagement solutions company. Anup pursued his Masters from Clemson University in Materials science and engineering. He has published research papers in JACS for work in Conductive Polymers and Crown Ether-based Li-Ion batteries for space applications.
4. Tell us how your company is contributing to the IoT / AI /Big Data Analytics / Robotics / Self-Driving Vehicles / Cloud Computing industry of the nation and how the company is benefiting the clients.
The mobility ecosystem is facing several challenges as a result of skyrocketing fuel prices, rising expenses for both skilled and unskilled manpower, and driver shortages. Amid tighter emission regulations and new players in the market, the entire ecosystem is getting increasingly complex. With competition piling up on multiple fronts, fleet operators must have cost control metrics aided by the right technology to minimize their bottom line while scaling business operations. There are various solutions in the market that have attempted to meet these pressing needs of fleet managers, but none of them has been comprehensive enough to deliver the necessary insights. The fundamental concerns that must be addressed are the health of the asset and operator behavior, which is where we step in.
Fleet managers are now able to preempt critical engine breakdowns resulting in lower downtimes and maintenance costs. Visibility on the route and application-specific performance aggregates are enabling them to better plan their operations with a focus on higher margins. Fuel pilferage alerts enable a direct reduction in trip overheads. Automated reports are reducing dependency on manpower. In a first-of-its-kind scenario in India, OEMs are leveraging ML for analyzing real-time data from retailed vehicles for improving products and providing proactive service in the truest sense.
5. Kindly mention some of the major challenges the company has faced till now.
Although Digital Twin technology lends itself to a wide range of commercial prospects across sectors such as manufacturing, aviation, and mining, we cannot occasionally pursue such opportunities. Moreover, with the scale of exponential growth, we are witnessing, we have had to let go of certain business opportunities due to the overwhelming demand from the mobility industry itself.
6. What are your growth plans for the next 12 months?
When it comes to EVs in the commercial vehicle market, we aim to be the face of telematics in India, from last-mile delivery to long-haul. In terms of technology, we intend to cement our position as forerunners in harnessing Digital Twin technology to serve use cases such as motor and battery health for Electric Vehicles. We are strengthening our position in the medium to heavy commercial vehicle category with IC engines (alternative fuels included) and expanding our analytics package to include health monitoring for diverse components, driver ranking, and advancements in automated reporting. While we are cementing our position in the Indian mobility ecosystem, the prospect of new opportunities in North America, Europe, Australia, and APAC is highly promising. Our remarkable expansion story exemplifies the game-changing potential of predictive analytics enabled by Digital Twin technology. We will continue our efforts to redefine performance benchmarks in mobility and transportation in FY’23.
7. How is the Big Data/AI/Robotics industry changing? What are some of the key technology transformations in this space?
Big Data, Artificial Intelligence, and statistics are gradually becoming more accessible to business planners. Individuals with limited knowledge of computational strategies can create robotic process automation with No-Code AI. In terms of hardware resources, microcontrollers are now able to support inferencing by machine learning models. Small footprint processors embedded in physical world assets used in everyday life are now capable of learning and reasoning with machine learning models.
Digital Twins are made using complex machine learning workflows that can be implemented on the cloud. They can imitate the real-world behavior of physical assets as virtual replicas to predict how they will behave.
Digital Twins normally require very heavy machine learning workflows that train on large payloads of data, but we have highly scalable cloud resources which enable that efficiently.
In the beginning, adoption was a major issue when it came to RPA (Robotic Process Automation) and AI, but with the evolution of technology, it has been proven that under routine operational conditions, AI is capable of taking statistically informed and faster decisions as compared to human cognitive intelligence. AI is expected to enrich disciplines across industries and everyday life
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