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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Michelin North America, Inc. has TreadVision by Michelin Retread Technologies at the Technology & Maintenance Council (TMC) Annual Meeting. This new approach transforms the retreading process by integrating artificial intelligence (AI), robotics and advanced data analytics to boost both the quality and uniformity of retreaded tyres, ultimately enhancing fleet operational efficiency.

A central component of this system is TreadEye. This advanced technology precisely evaluates tread depth by collecting 1,200 measurement points per tyre. It delivers accurate data on tread wear and casing condition, enabling fleets to determine optimal removal points, safeguard casing integrity and minimise unnecessary vehicle downtime.

The TreadVision process further incorporates proprietary automated inspections. These systems utilise AI and predictive modelling to detect subtle imperfections and anomalies that might otherwise be missed. The application of Vision AI to automatically interpret Casing Integrity Analysis results, specifically shearography, introduces a heightened level of objective, real-time quality control. This ensures that only casings meeting strict standards proceed through the retreading line.

In addition to inspection, the technology suite automates the physical handling and flow of tyres, which streamlines plant operations and can accelerate turnaround times. By automatically managing build specifications, TreadVision standardises production parameters, reducing variability and ensuring a more consistent final product.

These advancements in quality assurance and the reduction of human error are designed to produce more reliable retreads, directly supporting fleet uptime. The system is further enhanced by integration with Michelin’s Fleet Business Insights platform, which transforms operational data into actionable intelligence. Fleets gain clearer visibility into performance trends, asset tracking and cost control, optimising tyre management from first use through multiple retread lifecycles.

Janet Foster-Whitley, Senior Director, Enterprise Dealer & North America Retreading, said, “Michelin has a long history of innovation in the mobility space. With TreadVision, we’re driving the industry forward once again. Retreading plays a vital role in helping fleets extend asset life and control operating costs, and we’re evolving the process to deliver greater consistency, improved quality and faster turnaround times.”  

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MICHELIN Connected Fleet, the data-focused fleet management arm of Michelin, has introduced Smart Predictive Tire, a new monitoring solution specifically engineered for the trailers of Class 7 and 8 fleets. This technology is designed to shift trailer tyre management from a reactive to a proactive model by delivering real-time data on pressure and temperature, alongside predictive maintenance alerts. The goal is to empower fleet operators to address tyre health issues before they escalate, thereby minimising unplanned downtime, controlling costs and extending tyre life while enhancing overall vehicle safety.

At the heart of this innovation is Michelin’s proprietary Smart Leak algorithm, which is capable of identifying subtle, early indicators of tyre degradation. By flagging these warning signs promptly, fleet managers can intervene early, avoiding more severe and costly problems. The solution not only helps in preventing roadside emergencies but also supports broader operational efficiency. Maintaining correct tyre pressure through this system can lead to a reduction in fuel consumption and slower tyre wear, contributing to a more sustainable and economical fleet operation.

The effectiveness of Smart Predictive Tire has been evaluated through international pilot programmes in Europe, where participating fleets experienced notable improvements. Data from these trials showed a significant drop (up to 80 percent) in tyre-related roadside events, an increase in the usable lifespan of tyres (up to 9 percent) in cases where chronic under-inflation was previously an issue and measurable fuel savings (up to 4 percent) when optimal tyre pressures were consistently maintained. While these outcomes are promising, Michelin notes that individual results will depend on various factors unique to each fleet, including its size, operational routes and maintenance routines.

Integrated into the company’s Trailer Premium offer, the Smart Predictive Tire solution provides flexible deployment to meet diverse fleet needs, marking a step forward in connected vehicle technology.

Damon Newquist, Vice President – Sales, MICHELIN Connected Fleet, said, “Emergency roadside service continues to be a major pain point for fleets of all sizes, especially with trailers. When there is a tyre-related event, the root cause is overwhelmingly attributed to improper inflation. Michelin’s proprietary Smart Predictive Tire solution uniquely empowers fleet operators with the tools and alerts to address these issues before they become critical. These tools are designed to help extend tyre life, reduce costs and help keep drivers off the side of the road.”

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Triangle Tyre Co., Ltd. has been recognised as an ‘Excellence Level’ facility in the 2026 Shandong Smart Factory Cultivation Library, an accolade announced by the Shandong Provincial Department of Industry and Information Technology. This acknowledgment highlights the company’s significant progress and systematic achievements in intelligent manufacturing.

This provincial initiative is a key strategy to promote new industrialisation and merge the digital economy with the real sector. Enterprises were evaluated and ranked into three tiers – Pioneer, Excellence and Advanced – based on their comprehensive capabilities in digital design, smart production, lean management and sustainable operations. Over 30 businesses from the tyre sector and its related industries, including manufacturing, steel cord, rubber additives and machinery, were selected. Among these, 1 achieved the Pioneer level, 15 attained Excellence and 15 reached the Advanced level.

For years, Triangle Tyre has steadfastly advanced its intelligent manufacturing strategy, focusing on complete process digitalisation and smart system integration. Looking forward, the company remains committed to principles of innovation and green development. It plans to further integrate digital technologies with manufacturing processes, aiming to establish a modern production base that is not only smarter and more efficient but also safer and more environmentally sustainable.

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BANF, a Korean intelligent tyre system company, and Silicon Labs, the leading innovator in low-power wireless, have developed a tyre monitoring platform capable of real-time, high-resolution data processing specifically designed for autonomous vehicles and connected fleet operations. A detailed case study documenting this development is now available on the Silicon Labs website.

The system directly addresses the limitations of conventional Tyre Pressure Monitoring Systems (TPMS), which only trigger alerts after pressure drops substantially, leaving critical safety and efficiency issues undetected. BANF has transformed the tyre into an active intelligence node by integrating the Silicon Labs BG22 Bluetooth LE SoC into its in-tyre sensor architecture. This ultra-low-power system-on-chip was chosen for its robust RF performance, enabling reliable wireless communication even within the tyre's challenging environment where steel belts and thick rubber typically create a Faraday cage effect that impedes signals.

Inside the tyre, BANF's iSensor captures 3-axis acceleration, pressure, temperature and tread depth data at 4 kHz sampling rates. Rather than transmitting this raw information, the system performs onboard processing to extract key signals indicating wheel-nut loosening, slip events or reduced friction before sending concise alerts to the vehicle. This approach reduces communication load while accelerating response time. The integration of Silicon Labs' Secure Vault technology ensures automotive-grade security, protecting tyre data from tampering or spoofing for autonomous applications.

Power delivery has historically prevented advanced tyre sensing due to battery degradation from heat, centrifugal force and mechanical stress. BANF solved this through proprietary wireless power transfer technology. The Smart Profiler, mounted on the mudguard or fender, delivers continuous power to the iSensor using magnetic resonance, enabling battery-free operation with uninterrupted data acquisition at thousands of Hertz.

This real-time tyre intelligence feeds directly into chassis control, stability systems and autonomous driving algorithms for driverless trucks and buses where human intuition cannot detect traction loss. BANF plans to leverage accumulated data for predictive maintenance, route optimisation and insurance-linked services, positioning this solution as foundational infrastructure for next-generation mobility. Through this partnership, BANF and Silicon Labs have digitised the vehicle's last analogue domain.

Adam Sunghan You, CEO, BANF, said, "Tyres generate terabytes of data related to friction, load and mechanical stress, but until now there was no viable way to capture and transmit that information in real time. By combining Silicon Labs' BG22 with our wireless power technology, we have unlocked a new level of tyre intelligence."

Ross Sabolcik, Senior Vice President – Product Lines, Silicon Labs, said, "Compute is no longer confined to the CPU – it extends across intelligent peripherals and sensors. BG22 enables reliable, secure connectivity even in extreme environments, empowering innovators like BANF to digitise traditionally analogue systems."

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