As generative AI transforms the way consumers and businesses discover products, tyre manufacturers face a fundamental shift in digital marketing. Roshan Mohan, Co-Founder and CMO at FlowBlinq, and Founder of PCG argues that the next battle will no longer be fought on search engine rankings but on whether AI systems choose to recommend a brand in the first place.

For more than two decades, tyre manufacturers have refined their digital strategies around a familiar formula: optimise websites for search engines, invest in paid advertising, strengthen dealer networks and build visibility through reviews and comparison platforms. Success depended largely on securing a prominent position on Google’s search results.

That formula, however, is beginning to change.

The rapid adoption of generative artificial intelligence (AI) platforms such as ChatGPT, Gemini and Claude is reshaping how consumers search for information, compare products and make purchasing decisions. Rather than browsing multiple websites, customers are increasingly asking AI assistants to recommend the most suitable product based on their specific requirements.

For tyre manufacturers, this represents far more than another digital marketing trend. It fundamentally changes how products are discovered.

According to Roshan Mohan, Co-Founder and CMO at FlowBlinq, and Founder of PCG, companies that continue treating AI as simply another marketing channel risk missing a much larger transformation. “The customer journey for tyres has already started bending around AI, and the change over the next three to five years won’t be a redesign of the funnel; it’ll be a shift in where the funnel begins,” he says.

FROM SEARCH ENGINES TO AI CONVERSATIONS

Historically, buying tyres has been an information-intensive process. Consumers often compare technical specifications, dealer recommendations, user reviews, pricing and compatibility before making a purchase. Search engines have traditionally served as the starting point for that journey.

Generative AI is simplifying this process dramatically.  Instead of opening multiple browser tabs and manually comparing products, motorists can simply ask an AI assistant for recommendations based on vehicle type, budget, driving conditions and performance priorities. The AI then synthesises information from numerous sources into a single response. “What changes for the buyer is effort, not intent. They still want the right tyre for their car and budget, but instead of researching options and making the final comparison themselves, they are increasingly describing their needs to AI and letting it identify the best solution,” Mohan explains.

This shift effectively transfers much of the research process from the consumer to the AI model.

Industry forecasts suggest this transition is already underway. Gartner predicts traditional search engine volume will decline by 25 percent by 2026 as generative AI absorbs many queries that previously began with conventional search engines. Meanwhile, Checkout.com’s research indicates that consumers are embracing AI-assisted purchasing faster than many businesses are preparing for.

For tyre companies, the implication is profound: visibility may increasingly depend not on appearing first in search results but on being recommended within AI-generated answers.

THE RISE OF AI VISIBILITY

Search engine optimisation (SEO) has long centred on improving rankings through keywords, backlinks and domain authority. AI discoverability, Mohan argues, follows a very different logic.

“Traditional SEO was about ranking, winning a position on a page of 10 blue links. AI visibility, or Generative Engine Optimisation (GEO), is about being the answer rather than a link to the answer,” he says.

Unlike traditional search engines, large language models evaluate whether product information is sufficiently trustworthy, structured and complete before referencing it. If they cannot confidently interpret a manufacturer’s data, the brand may simply disappear from the recommendation altogether.

This places far greater importance on machine-readable product information than on conventional search optimisation. At the same time, AI systems are looking beyond a company’s own website to understand whether a brand is trustworthy. This makes it important for brands to have a presence across credible, independent sources, where editorial PR and genuine reviews can play a key role, rather than advertisements or advertorials. AI systems bring together these trust signals from multiple sources and present them to users in one place. This means the decision-making journey is increasingly shifting to the AI chat, where consumers can get a more comprehensive view before making a choice. Brands that build credibility across trusted sources will therefore be better placed to influence how AI systems recommend them.

Structured specifications, consistent product descriptions, schema markup and clearly organised technical information become essential because AI systems rely on these elements when generating responses.

FlowBlinq has developed what it describes as 17 Generative Engine Optimisation pillars to assess whether brands are sufficiently prepared for AI discovery. These include structured data quality, technical completeness and AI crawlability.

Perhaps more significantly, Mohan believes many companies have little understanding of how frequently AI platforms mention their products – or whether they are mentioned at all.

“Our citation tool runs a brand across ChatGPT, Claude and Gemini and shows, prompt by prompt, whether the brand gets cited, where it loses out to a competitor and where it’s simply absent from the answer altogether,” he adds.

TECHNICAL ACCURACY BECOMES A COMPETITIVE ASSET

Tyres differ from many consumer products because purchasing decisions depend heavily on technical specifications. Load index, speed rating, rolling resistance, wet grip, tread pattern and vehicle compatibility all influence suitability. Inaccurate recommendations can have genuine safety implications.

Mohan believes this makes structured product information particularly important for the tyre industry. “When product data is thin, a model doesn’t refuse to answer; it defaults to the brand it has the most confident, well-structured information about,” he adds. He warns that this tendency naturally favours manufacturers with richer digital product catalogues rather than necessarily those with superior products.

FlowBlinq’s research suggests considerable room for improvement. According to the company’s findings, 62 percent of Indian brand websites provide product descriptions that are insufficiently detailed for AI systems, while more than half lack product codes needed for accurate identification.

For tyre manufacturers, the solution is relatively straightforward but frequently overlooked.

Rather than embedding specifications within downloadable PDF brochures or image-based catalogues, companies should publish technical information directly on webpages in formats that AI systems can easily interpret.

Equally important is the broader digital reputation surrounding a brand. Mohan notes that AI systems increasingly rely on trusted third-party sources – including established news publications, Wikipedia and community platforms – to validate manufacturer claims before making recommendations.

AI ENTERS FLEET PROCUREMENT

The implications extend well beyond retail consumers. Business purchasing decisions often involve lengthy comparisons of performance, lifecycle costs, regulatory compliance and operational efficiency – precisely the type of structured analysis that generative AI performs well.

According to Mohan, procurement teams, fleet operators and original equipment manufacturers (OEMs) may adopt AI-supported purchasing even faster than retail buyers. “B2B tyre buying was never going to be immune to this, and it may move faster than consumer purchasing because procurement teams are exactly the audience generative AI tools were built to serve,” he says.

A fleet manager could ask AI to compare total cost of ownership across several tyre brands. An OEM purchasing team might request suppliers meeting specified rolling resistance or durability thresholds.

In such scenarios, manufacturers lacking accessible technical documentation risk exclusion before human procurement teams even begin formal evaluation. “The practical response isn’t a new sales deck. It’s making sure spec sheets, compliance documentation and comparative data exist in formats a model can read and trust,” Mohan says.

AI WILL ADVISE, BUT HUMANS WILL STILL DECIDE

While AI is poised to transform product discovery, Mohan believes the actual purchase decision will remain firmly in human hands – at least for high-value, safety-critical products such as tyres.

“I’d separate ‘AI helping me decide’ from ‘AI deciding for me’, because consumers still are the final decision makers,” he says.

Recent consumer research supports this view. While surveys indicate growing confidence in AI agents handling routine shopping tasks, willingness declines sharply when AI is expected to complete purchases autonomously. Most consumers remain comfortable with AI conducting research, comparing alternatives and shortlisting products but prefer to approve the final transaction themselves.

Tyres, Mohan argues, naturally fall into the category where human oversight will continue to matter.

“It’s a purchase people make infrequently, it carries real safety implications, and it typically involves a meaningful amount of money,” he says.

Consequently, AI is likely to dominate the research phase – evaluating specifications, warranty terms, prices and dealer options – while the final purchase decision remains with the customer.

However, one area where agentic commerce could quickly gain traction is in connecting customers directly with dealers. Rather than merely recommending a tyre, future AI assistants may also identify nearby retailers with available stock and book installation appointments automatically.

BECOMING AI-READY STARTS WITH THE BASICS

One of the most striking aspects of Mohan’s assessment is that the industry’s biggest challenge is not technological sophistication but digital housekeeping.

“It’s data, overwhelmingly, and it’s more basic than most companies expect,” he says.

FlowBlinq’s audits suggest that many corporate websites still lack the fundamental structure AI systems require. According to the company’s research, 91 percent of audited websites failed to provide clear information explaining their product catalogues in a way that AI could

understand. Even more concerning, nearly half were unintentionally preventing ChatGPT’s web crawler from accessing their websites because of security settings or plugin configurations.

“These aren’t strategic gaps; they’re operational oversights, and they’re fixable in weeks, not years,” Mohan claims.

For tyre manufacturers, this means that substantial improvements may not necessarily require major investments in new technology. Instead, they require a systematic review of how product information is organised, published and made accessible to AI systems.

Mohan also believes the next phase of digital readiness will involve preparing websites for agentic commerce by enabling real-time inventory visibility and ensuring AI systems can interact directly with product databases.

MEASURING RETURN BEYOND TRADITIONAL SEO

Digital marketing budgets have historically focused on search advertising, social media campaigns and marketplace optimisation. As AI-driven referrals grow, Mohan argues that businesses should begin allocating dedicated budgets towards AI discoverability.

“Yes, and the case for it is measurable rather than speculative now,” he says. Rather than relying solely on website traffic or keyword rankings, he believes organisations should monitor a different set of performance indicators.

Among the most important are how frequently AI systems cite a brand when responding to relevant queries, whether those citations are accurate and whether visitors arriving through AI recommendations convert differently from those originating through conventional digital channels.

Adobe’s Digital Insights research suggests AI-generated referrals are not only increasing rapidly but also producing stronger conversion rates than traditional referral sources. According to Mohan, this reflects the higher purchase intent of consumers who have already completed much of their evaluation through AI before visiting a manufacturer’s website.

TRUST WILL DETERMINE INFLUENCE

The emergence of AI recommendations inevitably raises questions about transparency. If AI systems become influential in shaping purchasing decisions, how can brands improve visibility without manipulating results?

For Mohan, the answer lies in accuracy rather than optimisation. “The honest answer is that AI-powered recommendations only work for a brand in the long run if they’re accurate, because these systems increasingly get checked,” he explains.

He believes manufacturers should resist the temptation to game AI systems through exaggerated marketing claims.

Instead, success will depend upon providing complete, verifiable product information that allows AI to make fair comparisons based on genuine performance characteristics.

“So the lever isn’t gaming a model into over-recommending you. It’s making sure that when a model compares your tyre honestly against a competitor on wet grip, rolling resistance or price, your data is complete enough that you win the comparisons you’re actually built to win,” he says.

In his view, transparency is not a constraint on AI marketing but its most durable competitive advantage.

FROM RECOMMENDATIONS TO TRANSACTIONS

The next evolution extends beyond recommendations. Emerging protocols are enabling AI systems to communicate directly with commerce platforms, inventory databases and pricing systems, allowing them to perform increasingly sophisticated purchasing tasks.

According to Mohan, this represents a significant opportunity for tyre manufacturers and dealers.

“The interesting shift is that AI agents are starting to interact with commerce systems directly... rather than just reading a webpage and stopping there,” Mohan says.

Once connected to live inventory systems, AI assistants could recommend the exact tyre that fits a customer’s vehicle, confirm stock availability at nearby dealers and compare prices in real time. Now, it can also make purchases directly from the chat window. This is something FlowBlinq is uniquely positioned to address as well.

Rather than generic recommendations based on previous purchasing patterns, personalisation could become highly contextual – considering vehicle compatibility, driving conditions, current inventory and even maintenance priorities.

However, Mohan cautions that these benefits will only be realised by organisations whose internal systems can support such interactions. Manufacturers and retailers will need modern, connected back-end infrastructure capable of sharing real-time inventory and pricing information with AI platforms.

ENGINEERING PRODUCTS – AND ENGINEERING DISCOVERABILITY

Looking ahead, Mohan does not believe AI will replace product quality as the defining competitive factor. Instead, he sees AI readiness becoming an equally important complement to engineering excellence.

“Product quality will always be table stakes; nobody wins on AI visibility with a mediocre tyre,” Mohan says. Yet he argues that superior products alone may no longer guarantee commercial success.

As purchasing journeys increasingly begin with AI conversations rather than search engines, brands that fail to present their technical information in formats AI systems can retrieve and trust may simply disappear from consideration.

“The winners will be the manufacturers who treated AI readiness as seriously as they treat product engineering,” Mohan says. He returns to Gartner’s prediction of declining traditional search volumes not as a warning but as an indication of how rapidly digital discovery is evolving.

“The discovery layer is moving to AI faster than most manufacturers’ data infrastructure is moving with it,” he says.

His concluding observation perhaps best captures the industry’s emerging challenge.

“A brand can make the best tyre in its category and still lose the sale simply because it was invisible in the one conversation the buyer had before deciding. That’s a genuinely new way to lose, and avoiding it is now a core marketing responsibility, not a technical footnote,” Mohan says.

Swarnakaruna Princeton Rubber Industries has built capacity to recycle about 96,000 end-of-life tyres annually after government-backed financing helped the Visakhapatnam company transition from processing buffing waste to recycling whole tyres. The recycler is now targeting larger industrial buyers while exploring tyre imports to lower raw-material costs.

India’s push to formalise its end-of-life tyre recycling ecosystem is creating room for smaller recyclers to scale, but access to capital, machinery reliability, raw-material costs and the economics of producing higher-grade crumb rubber remain significant constraints.

Visakhapatnam-based Swarnakaruna Princeton Rubber Industries Private Limited offers a case in point. The company has expanded from a small operation processing buffing powder to a whole-tyre recycling business capable of producing around 300–400 tonnes of crumb rubber a month following financial support channelled through IFCI Venture Capital under a government-backed programme for Scheduled Caste entrepreneurs.

The business traces its roots to 2014, when Managing Director N Swarnalatha established K.S. Princeton Rubber Industries after approaching the District Industries Centre in Visakhapatnam with plans to become an entrepreneur.

The original operation had a single grinder and relied on buffing powder as its principal feedstock because it lacked the equipment required to process complete end-of-life tyres.

“The company started in 2014, but the setup was very minimal. We had just one grinder and were not capable of recycling a whole tyre, so buffing powder was our raw material,” said Swarnakaruna Princeton Rubber Industries Chief Executive Officer Ashish Kumar Isukapati.

That model began changing in 2018 when the company sought funding from IFCI Venture Capital Limited. The proprietorship was subsequently converted into a private limited company as part of the process of securing the investment. Funding was eventually released in stages, with commercial production from the expanded facility beginning in August 2024.

According to Isukapati, the project received about INR 50 million from IFCI Venture Capital, alongside a promoter contribution of INR 17.1 million. The overall project cost was approximately INR 67.21 million.

The money was deployed largely towards machinery and rebuilding and expanding the existing manufacturing premises.

Isukapati said the financing was particularly significant because conventional access to growth capital can prove difficult for first-generation entrepreneurs.

TYRES TO CRUMB

The expanded operation can process roughly 8,000 end-of-life tyres each month, equivalent to around 96,000 tyres annually, according to Isukapati. At the upper end of production, the facility can manufacture around 400 tonnes of crumb rubber a month, while recovered steel accounts for roughly 25 percent of output by weight or around 100 tonnes at that production level.

The company currently sells entirely into the domestic market, with Kerala representing its largest destination. It also supplies TVS Sundaram Industries in Chennai.

However, the economics vary considerably according to crumb size and end application.

More than 70 percent of the company’s current crumb-rubber production goes to non-tyre applications, particularly rubber-mat manufacturers in Kerala. These customers typically consume 20 and 24 mesh material. TVS Sundaram Industries takes roughly 60–80 tonnes a month, with the company supplying 30-mesh crumb rubber.

Tyre-related customers typically require finer grades such as 40, 60 and 80 mesh crumb rubber. The company has pulverising equipment capable of producing material in those ranges, but finer grinding lowers throughput and changes the economics of the operation.

“We are capable of manufacturing the higher meshes, but the overall output comes down. We have to balance which mesh we manufacture against the quantity we can produce because crumb rubber itself is a relatively low-priced product. The way to generate sustainable revenue is through volume,” Isukapati said.

That volume-quality equation has become particularly important as the recycler seeks to tap larger customers closer to its Visakhapatnam plant.

One of its main targets is HINCOL, which Isukapati said operates a facility locally and requires around 500 tonnes of crumb rubber a month. The company has so far been unable to fully address that opportunity because of the combination of volume, quality specifications and tender pricing.

Isukapati sees similar opportunities with buyers across the eastern and southern tyre and rubber manufacturing belt. He estimates that potential demand within roughly 300 kilometres of its operation could exceed 1,000 tonnes a month.

The challenge is converting that proximity into profitable sales.

PORT OPPORTUNITY

The company currently sources a significant portion of its tyres through Andhra Pradesh State Road Transport Corporation auctions. And the company is also seeking a licence to import end-of-life tyres, a move it believes could allow it to leverage its proximity to Visakhapatnam port and reduce feedstock costs.

“We are trying to capitalise on Visakhapatnam port and have applied for an import licence. If that works out, we could spend less on raw material, which would give us an advantage in offering a more competitive price,” Isukapati said.

The company’s expansion has not been without setbacks. Location presents a significant constraint. The facility operates on a plot of about 980 square yards, where incoming tyres, finished crumb rubber and recovered steel all have to be stored and managed. Expanding towards the 500-tonne monthly volumes sought by larger customers could therefore require the company to address its space limitations.

Technical support and spare-part availability are also more difficult in Visakhapatnam than in established manufacturing clusters, Isukapati said.

BIGGER CHALLENGE

Swarnakaruna does not currently operate a dedicated research and development department. Product development and process adjustments are handled by its production manager and technical staff, while third-party laboratories are occasionally used when customers require test reports.

Beyond the company’s own expansion, Isukapati sees feedstock pricing as one of the biggest problems confronting India’s mechanical tyre recyclers.

Waste-tyre prices can rise when pyrolysis operators increase production, but crumb-rubber producers cannot necessarily pass those higher costs to customers because buyers have multiple competing suppliers.

He also called for greater policy attention to the procurement and channelisation of waste tyres, arguing that mechanical recyclers do not necessarily receive a commercial advantage for converting end-of-life tyres into reusable rubber products.

His comments come as India’s tyre-recycling ecosystem adjusts to extended producer responsibility requirements and increasing competition for waste-tyre feedstock.

For the tyre recycler, the next phase will therefore be less about simply adding equipment and more about achieving scale economically. The company is seeking cheaper feedstock, higher production, access to larger nearby customers and potentially a more formal research and development structure.

Recovered steel also remains important to the economics of the operation. Despite the constraints, he sees a sizeable addressable market around the company’s Visakhapatnam base.

“The opportunity is absolutely there. If we can get the right balance between the mesh we manufacture, the quantity we produce and the price, there is a huge opportunity out there,” Isukapati said.

DUNLOP (company name: Sumitomo Rubber Industries) has confirmed that its proprietary SENSING CORE technology has been adopted for medium-duty truck models. The system, designed to detect the conditions of tyres, vehicles and road surfaces, is fitted to Isuzu Motors' FORWARD medium-duty trucks with a gross vehicle weight exceeding 14.5 metric tonnes, which come standard with the Wheel Detachment Prediction Detection System, one of the functions offered by SENSING CORE.

SENSING CORE is a software technology built on expertise gained from the Deflation Warning System, a tyre pressure warning system. It analyses wheel speed data generated by tyre rotation together with controller area network data transmitted through the vehicle's control system, allowing it to detect various tyre and road conditions, including tyre pressure, wear, load and signs of potential wheel detachment. The Wheel Detachment Prediction Detection System in Isuzu's FORWARD models detects loosening of wheel nuts while the vehicle is in motion, alerting the driver through a warning message and buzzer if an abnormality is found.

This capability complements conventional inspections and driver experience and is expected to play a significant role in preventing wheel separation accidents. Isuzu's FORWARD is a medium-duty truck with a broad lineup designed to meet diverse logistics industry needs, and such trucks are widely used in local delivery, long-haul transport and building and construction operations, serving as a vital means of transportation.

Operated across urban areas and major highways, wheel detachment accidents involving these trucks could cause serious harm to third parties such as pedestrians and cyclists, driving growing societal demand for improved safety. Preventing such accidents requires fundamental inspection and maintenance measures, yet drivers find it difficult to detect loosening wheel nuts while driving, making early-detection technologies essential. The provision of the Wheel Detachment Prediction Detection System to medium-duty trucks enables early detection of signs of vehicle malfunctions and contributes to further improving safety and preventing wheel detachment accidents.

SYMX.AI, a developer of artificial intelligence technology for mobile industrial equipment, has raised USD 6 million in Series A financing led by Ascent Capital Partners and agreed a global technology and commercial partnership with Yokohama Off-Highway Tires.

The Canadian company develops software and hardware for mining, construction and forestry operations. Its platform provides connectivity in areas without network coverage, integrates data from equipment made by different manufacturers and supports fleet optimisation.

Its products cover dispatch, fleet management, enterprise asset management and tyre life management, as well as equipment health, fuel consumption, haul road intelligence, inventory and operator performance. The company also holds patents covering its internet of things and AI technologies, including a patent for its “Greenhouse Gas Emissions Data Collection and Cross-Validation System”, granted in 2026.

SYMX.AI has also launched an AI-powered inspection platform and a chatbot for industrial operations.

“Physical AI has been struggling in mining for simple reasons. The machines are mobile, mixed-fleet data is vendor-locked, and most of the industry has limited to no network coverage. Intelligence needs a body and a nervous system before we deploy a model. Even today, assembling that foundation takes seven or eight vendors and up to twelve months. We deliver it in a week. We built the hardware and the connectivity first, which is why our AI layer delivers real results. I am glad that capital is finally flowing to atoms again, and mining will finally get what it has long deserved,” said Ash Agarwal, CEO of SYMX.AI.

Technology tested in mining operations

SYMX.AI said it had validated its technology through projects across the mining value chain. At Glencore's Onaping Depth project in Ontario, Canada, the company demonstrated battery monitoring, short-interval control and fleet optimisation for battery-electric vehicles.

“The problem SYMX.AI solves is foundational,” said Peter Xavier, Chief Operating Officer of Canada Metals, a Glencore company. “Connectivity and cross-OEM data integration at the point of work are infrastructure gaps the mining industry has struggled with for decades, but SYMX.AI changes the paradigm. Our proof-of-concept validation of the SYMX.AI platform on our OEM BEV equipment successfully supports underground short-interval control, battery monitoring and fleet optimization. This level of integration and insight is exactly what the industry needs to move forward.”

Global partnership with Yokohama

Under the partnership, Yokohama Off-Highway Tires will offer the integrated hardware and software platform to customers through its international commercial network. SYMX.AI will supply the internet of things hardware, connectivity, cloud platform and intelligence capabilities.

Customers will be able to adopt the technology as a standalone tyre intelligence solution or as part of a broader fleet optimisation platform.

“The tyre technology market is crowded with TPMS providers, but most systems monitor pressure and temperature while offering little insight beyond those measurements. Operators are asking a more important question: can the same system help us increase production, protect valuable assets such as tires and reduce fuel consumption? SYMX.AI has built a complete platform that addresses this market need by connecting tire condition with equipment performance, haul road conditions and operator behavior. This helps sites reduce costs, extend tire life and improve overall fleet performance,” said Loïc Ravasio, President of Yokohama Off-Highway Tires. “By working with SYMX.AI and offering flexible business models, Yokohama OTR is becoming a true digital partner to the industry, helping customers optimize their operations.”

Expansion and investment

SYMX.AI is expanding its product range with an AI-driven dispatch and fleet management system and an enterprise asset management platform based on live machine sensor data and fault codes.

The asset management system combines equipment health monitoring, diagnostic alarm management, oil analysis and work order management. The company says the system is designed to reduce reliance on fixed maintenance schedules and manual data entry.

Its products integrate with AVEVA Connect, SAP, Oracle and existing data historians, allowing operators to add the technology without replacing their existing systems.

The company said it supported open-pit and underground operations in Kazakhstan, the US, Mexico, Europe and Asia.

“We have watched this team for three years demonstrating grit, perseverance, and commitment. They build things that work in places most software never reaches, and the customers keep renewing worldwide. That is rare in this industry. The comprehensive IoT, connectivity, and AI app layer is deployed at difficult sites, and I’m glad that the market is finally catching up,” said Jonathan Loeffler, Co-Founder and Managing Partner of Ascent Capital Partners.

The financing will support international deployment, development of the AI platform and a broader range of wireless communication products, SYMX.AI said.

Anyline, a global leader in mobile AI and intelligent data capture technology, has rolled out version 1.9 of TireBuddy, featuring a redesigned inspection report that aims to help customers more easily understand the condition of their tyres and place greater trust in technician recommendations. The updated report is intended to make the information gathered during an inspection simpler for customers to digest.

The report presents tread depth, tyre age and estimated braking distance to convey overall tyre health, while make, model, size and seasonal markings identify exactly which tyres are mounted on the vehicle. The new version also allows technicians to attach images of sidewall and rim damage to the report, transparently documenting issues that require attention so customers can have confidence in the recommended work.

Technicians can now record rim and tyre damage, such as scratches on rims and bulges on sidewalls, during the inspection workflow and display it in the report. Photo evidence of rim and sidewall damage can be attached to technician notes and added to the report. A not-roadworthy flag can also be set for a tyre with healthy tread depth but other serious defects, showing in the report as recommended for immediate replacement. All existing and new elements continue to fit on a single page, which can be saved as an image or PDF for sharing with the customer.

The TireBuddy report addresses a critical issue for workshops by ensuring customers fully understand inspection findings. Verbally conveying such information is not always easy, and traditional inspection forms tend to be dense and text-heavy. Everything a technician finds in the bay needs to be detailed in the report the customer sees, which should serve as visual support for service recommendations. With version 1.9, evidence appears alongside the recommendation, and photos of tyre or rim damage are inserted with other tyre information so customers can recognise findings without entering the bay.

Damages are categorised alongside visual evidence, so a bulge is recorded consistently regardless of technician, location or shift, turning individual reports into a comparable record that still captures the state of every tyre on the inspection day. From version 1.9, every report can carry damage, roadworthiness and photo evidence alongside the tread reading. Smaller changes include an optional pair replacement setting that automatically marks the paired tyre on the same axle for replacement when one tyre is not OK, up to 10 custom fields per account in the vehicle identification step for work order or storage numbers and a redesigned vehicle identification screen consistent with the tyre identification screen.

Lukas Kinigadner, CRO, Anyline, “Every replacement recommendation is a request for trust. With 1.9, the technician doesn't have to ask for it. The damage is on the report, photographed, next to the tyre it belongs to.”