Artificial intelligence (AI) is beginning to reshape fleet management beyond conventional telematics that merely track vehicles. In India’s fragmented trucking ecosystem, where cost pressures, ageing fleets and operational inefficiencies remain persistent challenges, AI-led platforms are attempting to shift the industry from reactive monitoring to predictive decision-making. Mumbai-based Taabi Mobility Limited is among the companies advancing this shift, using large-scale data analytics to link driver behaviour, vehicle performance and operating conditions, offering fleets actionable insights aimed at reducing costs, improving safety and optimising asset utilisation.

Generally, most fleet management platforms track location, speed and unauthorised stops, making them mainly descriptive and not prescriptive. Mumbai-based Taabi Mobility Limited is changing the narrative leveraging the computing and predictive power of artificial intelligence (AI).

“Our AI solution adds value by correlating thousands of variables like driver behaviour, road conditions, load, ambient temperature, tyre age etc. and continuously learning in real time. It predicts outcomes. Moreover, traditional reports are static, while AI gets more accurate over time, adapting to different routes. Threshold alerts are not just fixed values. AI detects unusual rates of change and alerts proactively,” explained Chief Executive Officer Pali Tripathi.

Alluding to whether the AI platform only analyses data or also guides operators in real time, she explained that alerts differ by user. “Drivers get in-cabin voice alerts about tyre pressure, fatigue, collision risk etc. Fleet operators receive aggregated, actionable insights across many trucks via a live dashboard with critical exceptions highlighted,” Tripathi said.

She added that the effectiveness of AI relies on high-quality data. The control tower suggests actions like contact drivers, schedule maintenance or recommend coaching but does not fully automate vehicle control. Alert volume is configurable to prevent human fatigue.

She noted that the company’s solution also provides specific corrective actions. “A truck from Delhi to Jaipur showing left-tyre vibration and slow pressure drop triggers an alert for the driver to stop at the next halt. Fleet managers are also notified. The system identifies the issue, potential cause and suggested solution, not just the symptom,” explained Tripathi.

Tripathi contended that the fleet management sector in India is seeing multi-modal transport hubs, digitisation, improved road and waterway connectivity and better warehousing and last-mile efficiency. However, the industry is still not fully organised like in developed countries.

Taabi, she explained, is an operations intelligence platform designed to reduce total operational costs per truck by predicting issues rather than relying on fixed schedules. The system monitors vehicle behaviour, load, road conditions and tyre pressure to flag problems early.

“While fleets focus on fuel cost, tyre health directly impacts safety and performance. Fleet interest in tyre solutions is usually part of a holistic cost-reduction strategy rather than a standalone concern. A 10 percent improvement in tyre life can save crores of rupees for large fleets, making investments in platforms like Taabi worthwhile,” said Tripathi.

Companies in last-mile logistics and cement or steel transporters actively track these metrics through Taabi’s solution.

When asked about collaboration with tyre manufacturers and vehicle OEMs for data sharing, Tripathi indicated that such partnerships are still evolving and not yet fully formalised. She noted that major commercial vehicle OEMs along with tyre manufacturers already collect operational data independently for research and product development.

However, the company’s platform currently prioritises a customer-first approach, focusing on empowering fleet operators with actionable insights. Instead of directly supplying data to OEMs, the system enables fleets to use operational intelligence to hold manufacturers accountable for vehicle performance.

FROM GROUND UP

The company currently serves around 1,300 fleet operators across India. Growth is measured in assets deployed rather than just customers, as a single vehicle may use multiple solutions such as OBD devices, video telematics and fuel monitoring systems. Average deployments are about 272 assets per fleet with ranges from 50 to 4,000 assets.

The company has recorded 130–132 percent year-on-year growth, largely driven by expanding deployments within existing customers.

Nonetheless, Tripathi explained that the primary hurdle for the company was building trust in a completely new category of product. “Since fleets had operated for decades without such technology, convincing operators that the platform could deliver measurable value was difficult. We therefore positioned AI not as a replacement for human judgment but as a tool that enhances decision-making, highlighting hidden operational costs such as tyre wear, vehicle inefficiencies and the financial impact of driver behaviour,” she averred.

Another major challenge was the data ‘chicken-and-egg’ problem. AI systems require large datasets to function accurately, but fleet operators were hesitant to adopt the platform without proof of performance.

Although the company had access to global data, it began collecting India-specific road, load and operational data three to four years before launch to train its models. Early adopters and pilot customers were told transparently that the system would improve as more local data was gathered.

A further complexity involved customising the user interface and experience for different sectors. Construction fleets, buses, trucking companies and enterprise operators such as ambulance services all required different dashboards and operational insights. As a result, persona-based interface design became an important part of product development. When discussing adoption among smaller fleet operators, Tripathi noted that fleets with 5–20 trucks typically adopt the solution through larger enterprises or ecosystem partners.

To improve accessibility, the company offers subscription-based pricing similar to mobile phone plans, avoiding large upfront costs. The base plan provides simple alerts and WhatsApp-style notifications. More advanced features are included in Gold and Platinum plans, which deliver deeper analytics and operational insights.

IMPLEMENTATION

Addressing the challenge of deploying AI-based fleet monitoring on older commercial vehicles, Tripathi noted that a large share of India’s truck and bus fleet is 10–20 years old, meaning many vehicles lack factory-fitted OBD or tyre pressure monitoring systems (TPMS).

“To overcome this, we use a matchbox-sized device that plugs into aftermarket OBD ports typically available on trucks manufactured after 2000. The device captures key operational data such as engine performance, speed, RPM, load conditions and fuel consumption,” she noted.

For older vehicles without such capabilities, additional hardware such as fuel tank sensors are installed to track consumption and detect issues like fuel theft or reverse draining. The system can also monitor gensets and auxiliary equipment, while video telematics can be added when required.

Tripathi explained that this approach can actually make the platform particularly valuable for older fleets, enabling both small and large operators to access AI-driven monitoring and predictive maintenance.

The platform also supports intelligent cameras inside the cabin and facing the road, enhancing driver behaviour monitoring and safety analytics. For tyre monitoring, fleets can use external TPMS units, although these are relatively expensive. As a cost-effective alternative, the system derives proxy performance indicators from OBD data and telematics to estimate tyre health and vehicle performance.

“In minimal deployment scenarios, even a driver’s smartphone can provide basic telematics functions such as GPS tracking, route adherence, geo-fencing and idle detection, enabling gradual adoption of digital fleet management tools,” noted Tripathi.

The platform follows strict data security and privacy standards. All operational data is end-to-end encrypted using AES-256 and stored on cloud infrastructure within India through Microsoft Azure. Fleet data remains private to each operator, meaning one fleet cannot access another’s information.

Internally, only aggregated data is used for model training without exposing raw fleet-level details. Any external data sharing is tightly controlled and compliant with India’s Digital Personal Data Protection framework.

MARKET DEMAND

The company views the retrofit segment as the largest opportunity in India, as most commercial vehicles are older and new truck sales represent only a small share of the total fleet. Its strategy is to democratise access to fleet intelligence by enabling AI-driven monitoring on existing vehicles rather than waiting for fleet modernisation.

“We also see growing relevance in commercial EV fleets, particularly in last-mile delivery networks. Our platform acts as an intelligence layer for mixed fleets transitioning from diesel to electric vehicles, helping operators evaluate return on investment, identify suitable routes for EV deployment and manage operational economics. Vehicle-agnostic solutions such as video telematics can be deployed across cars, vans and EV delivery vehicles,” Tripathi contended.

Rather than relying solely on hardware innovation in tyres or vehicles, the company focuses on AI-driven insights derived from sensor data. “Continuous monitoring allows our system to predict performance issues and recommend interventions. The platform functions as an operational intelligence layer, offering voice-based guidance for drivers, cost-optimisation insights for fleet owners and operational support for fleet managers,” averred Tripathi.

Devices installed in vehicles perform round-the-clock monitoring of engine, fuel, tyre and other operational parameters, delivering predictive alerts and actionable insights. By simplifying complex data into clear recommendations, the AI platform aims to improve fleet efficiency, reduce costs and enable smarter operational decisions.

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Yokohama Rubber has established a new research and development centre in Hangzhou, China, as the Japanese tyre maker seeks to strengthen localised product development and speed up response times in the Chinese market.

The new facility, named Yokohama China Technical Center, began operations in May within the company’s new passenger car tyre plant in Hangzhou, which started production in November 2025.

The company said the centre would enable the local development of products specifically for the Chinese market, from initial research through to completion, helping to accelerate product launches and improve responsiveness to regional demand.

The centre will consolidate R&D functions for Yokohama Rubber’s tyre and multiple business divisions in China, while expanding engineering staff and testing facilities. Its activities will include tyre development, raw material analysis and evaluation, supplier audits, and mould drawing preparation.

Yokohama Rubber said the new operation would also support research into new raw materials and the development of local suppliers in China.

The company currently operates tyre plants in Hangzhou and Suzhou, alongside multiple business plants in Hangzhou and Weifang.

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Chhattisgarh-based technology company Aarika Innovation has introduced KoolWheel, an automated tyre water spray cooling system manufactured in India.

The product is designed for freight vehicles and school buses to manage tyre overheating caused by road surface temperatures.

The system uses IR (infrared) temperature sensors, a 5-bar pump and solenoid valves to spray a mist on tyres when temperatures exceed a threshold. The hardware operates on a 12V setup and includes a controller that requires no driver intervention. Dashboard indicators and buzzers provide alerts regarding system status and temperature levels.

The company has introduced two variants of the product for KoolWheel Freight, which is designed for trucks, trailers and multi-axle vehicles, covering up to 22 tyres across six axles. And KoolWheel SchoolSafe, which is developed for school buses and coaches, featuring a 50-litre stainless steel tank and an automatic shutoff to prevent battery drain.

The company states the system can reduce tyre temperatures by up to 25deg Celsius and extend tyre life by up to 35 percent. The technology is intended to reduce the risk of blowouts and maintenance costs for fleet operators. The product is currently available in markets including Chhattisgarh, Madhya Pradesh, Maharashtra, Uttar Pradesh, Rajasthan and Telangana.

Swayam Agarwal, Founder, Aarika Innovation, said, “KoolWheel has been created to solve a very real problem faced by Indian transporters and school bus operators every day. Tyre overheating is not just a maintenance issue; it directly impacts road safety, operating costs, and fleet reliability. With KoolWheel, our aim is to offer an affordable, intelligent, and Made-in-India solution that helps fleets run safer, longer, and more efficiently.”

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European tyre major Pirelli is starting production of its Cyber Tyre technology at its plant in Georgia. The facility produces tyres for the US market, including products for the motorsport segment.

The announcement occurred during the SelectUSA Investment Summit. Cyber Tyre is a system that collects data from sensors embedded in tyres. This data is processed through software and algorithms to communicate with vehicle electronics. The system is intended to integrate with driving systems to provide functionalities for mobility and safety.

Pirelli is also introducing the Modular Integrated Robotised System (MIRS) at the factory. This manufacturing process uses robots to manage productivity and quality. The system creates a link between product design and application. This update is intended to increase the production capacity of the site.

The Georgia plant has operated for over two decades and includes a research and development centre. The facility uses natural rubber certified by the Forest Stewardship Council.

Claudio Zanardo, CEO of Pirelli North America, said, “The start of Cyber Tyre production in our Rome, Georgia plant is a significant milestone for Pirelli in this country. It reflects our commitment to bringing advanced technologies like Cyber Tyre closer to the market, further strengthening our industrial footprint and innovation capabilities in the United States.”

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The Yokohama Rubber Co., Ltd. developed a proprietary tyre mould design support system in April 2026, integrating finite element method (FEM) simulations and the company’s own artificial intelligence technology. This new tool is designed to augment the expertise of development personnel, enabling even less experienced staff to efficiently design moulds. It achieves this by providing data derived from numerous virtual experiments, which clarify how different mould design factors influence tyre characteristics.

The system accelerates mould development, lowers costs and minimises the rework typically associated with realising new designs. Furthermore, by fostering a multi-perspective understanding of the links between mould design elements and tyre performance, the tool equips Yokohama Rubber’s developers with fresh insights. These discoveries are expected to aid in creating tyres capable of achieving higher performance levels.

Developed under Yokohama Rubber’s HAICoLab AI concept launched in October 2020, the system addresses longstanding challenges. Mould design critically affects tyre traits, but traditionally understanding this relationship required expensive, time-consuming trial production and evaluations. The process also depended heavily on the tacit know-how of highly experienced staff, leading to variations in accuracy and development time based on individual expertise.

The support system resolves these issues through automated simulations and AI-based prediction and visualisation. It first generates numerous tyre FEM models with varied mould shapes and calculates their characteristics in a virtual space. These results train an AI surrogate model that instantly predicts design factor-performance relationships. By applying explainable AI technologies like SHAP and Partial Dependence Plots, the company’s developers can quantitatively visualise each factor’s impact, easily determining necessary adjustments to achieve targeted tyre characteristics.

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