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In an era where fleet operations are challenged by dynamically evolving emissions norms, rising fuel costs, and freight availability constraints, Aman Singh offers perspective on how predictive analytics can address these challenges. Singh, founder and head of analytics at Intangles, notes that these growing complexities in global markets have created a pressing need for advanced diagnostics to keep fleets running efficiently and costeffectively.
“We fill the gap between simple warnings and realtime diagnostics,” Singh explains. Intangles’ approach doesn’t merely respond to mechanical faults—it predicts them. Leveraging physicsbased artificial intelligence, Intangles processes multiple data points simultaneously, emulating expert mechanics’ diagnostic capabilities. Singh succinctly captures the essence of this innovation: “Our AI models think like expert mechanics, catching problems early, before they have the chance to cause trouble.”
The tangible benefits of such predictive diagnostics are compelling. Fleets adopting Intangles’ technology typically experience an 810% increase in fuel efficiency. This substantial improvement is vividly illustrated through realworld examples, such as early detection of turbocharger issues that yield similar fuel savings. A Florida transit agency serves as another case in point, significantly reducing idling time and swiftly identifying fuel tank contamination. “Fleets adopting our technology typically see an 810% boost in fuel efficiency,” Singh underscores, highlighting the direct economic and environmental impact of predictive analytics.
Beyond traditional diagnostics, Intangles employs digital twins, virtual replicas of individual vehicles, to customize maintenance predictions precisely. These digital counterparts learn from and adapt uniquely to each vehicle’s operational context, thus optimizing maintenance and extending vehicle lifespan. Singh emphasizes, “Every vehicle’s digital twin learns and adapts uniquely to its environment,” reinforcing the transformative potential of tailored predictive maintenance.
Not confined to trucking alone, Intangles’ technology finds potential applications across aviation and rail industries, drawing inspiration from proven aviation predictive models. Currently, Intangles is actively engaged in locomotive projects, including innovative hybrid battery systems. Singh encapsulates this ambitious expansion succinctly: “We’re applying aviationinspired predictive tech to trucks and beyond.”
A significant aspect of Intangles’ solution lies in its capacity to standardize diverse sensor data from multiple original equipment manufacturers such as Bosch and DENSO. By training AI models specifically for each unique vehicle configuration and leveraging global datasets, Intangles ensures consistently high accuracy. “Our models seamlessly handle diverse sensors across different manufacturers,” Singh notes, “allowing fleet operators to benefit from consistent insights even when managing mixedbrand vehicle fleets.”
Looking ahead, Intangles introduces an innovative PredictiveQuantitativeRecommendation (PQR) framework—a gamified, conversational AI aimed at enhancing fleet management usability. In addition, the company is addressing the critical issue of range anxiety in electric vehicles through precise forecasting and recommending optimal nearby charging stations. Singh confidently states, “We’re solving EV fleet anxiety by accurately forecasting driving ranges—so fleet managers can stop worrying and start enjoying the benefits of smooth operations and fuel savings.”
Ultimately, Intangles not only optimizes fleet efficiency and significantly reduces operational costs but also mitigates breakdown risks, underscoring sustainability through advancements in EV support. As fleets globally grapple with complexity and sustainability challenges, Intangles stands poised to reshape the industry landscape with its innovative predictive solutions.
