EyeVi uses AI-driven digital twins to automate road data capture, cut maintenance costs, and expand in the U.S.

EyeVi, an Estonian startup, is pursuing a bold shift in how roads are surveyed, maintained, and expanded. The company plans to build out tools that automate road data capture to improve maintenance and operations, with an eye toward expanding into United States markets. EyeVi delivers computer-vision hardware paired with AI-driven software-as-a-service to map and identify road infrastructure needs for road service surveyors, repair crews, and municipalities. The overarching aim is to provide a scalable platform that can continuously monitor road conditions, support proactive maintenance, and reduce the cascading disruptions caused by roadwork. EyeVi’s vision centers on transforming how authorities and contractors understand road networks, from data collection to decision-making, by enabling faster, more accurate, and more cost-effective insights.

EyeVi’s mission, technology, and early funding trajectory

EyeVi emphasizes a core capability: surveying roads at roughly one-hundredth the cost of traditional manual approaches. This cost reduction target is echoed in the company’s claims about the efficiency gains possible when automation and AI are applied to road data collection, as opposed to labor-intensive manual surveys that require teams traversing extensive road networks. The company’s leadership has framed the platform as a continuation of an idea that originated about a decade ago, initially conceived as a driver for Google Streetview. Gaspar Anton, EyeVi’s chief executive, describes the platform as capable of surveying vast stretches of road with a level of scalability and speed that was previously unattainable with conventional methods. The seed funding EyeVi has secured—approximately $2 million—marks an early validation of the concept and a signal of confidence from investors that a digital transformation approach to road maintenance could yield meaningful economic and operational benefits. With this capital, EyeVi intends to extend the same underlying concept used in road surveying to broader road operations and maintenance applications, positioning itself to support both public agencies and private-sector partners as they adopt AI-enhanced road data solutions.

The company’s stated ambition includes expansion into U.S. markets, a strategic move that aligns with the scale and diversity of road networks in the United States. By entering the U.S. market, EyeVi seeks to demonstrate the platform’s versatility across different regulatory environments, climate conditions, and road-building practices, while also tapping into a substantial demand for more efficient asset management in a system characterized by aging infrastructure and tightening budgets. The founder’s narrative about leveraging a “digital twin” approach—creating a comprehensive, data-rich representation of road networks that can be continuously updated as conditions change—serves as a foundational theme for EyeVi’s business proposition. This approach envisions a shift from episodic surveys to continuous monitoring, enabling decision-makers to simulate how traffic patterns, weather events, and varying maintenance strategies might influence road performance over time. EyeVi’s early fundraising underscores the market’s appetite for AI-powered infrastructure tools that promise to reduce costs while enhancing reliability and safety for road users.

The seed round was led by ff Venture Capital, a well-known venture firm that has supported a range of technology-enabled businesses. Participating investors included RKKVC, Decacorn Capital, Iron Wolf Capital, Superangel, Spring Capital, Kaamos Group, and several Estonian business angels, notably Väino Kaldoja, founder of AuveTech, and Taavi Rõivas, the former prime minister of Estonia. This diverse syndicate reflects cross-border interest in how AI, computer vision, and digital twin concepts can be applied to large-scale, capital-intensive industries such as road construction and maintenance. EyeVi’s stated objective—scaling its service from tens of thousands of miles surveyed in the recent past to a target of one million miles—signals an aggressive growth plan that relies on both product development and geographic expansion. In short, EyeVi’s early funding narrative is that of a tech-driven, cost-focused platform aimed at transforming a traditionally labor-intensive sector through automation and data integration, with a clear ambition to prove its value in the U.S. market and beyond.

EyeVi’s emphasis on automating road data collection is complemented by a broader industry trend toward digitally enabled road management. The company positions itself within a spectrum of players seeking to apply AI, computer vision, and cloud-based analytics to road surveys, maintenance planning, and asset management. The seed round’s composition reinforces a view that both European and North American investors see potential for cross-border deployment of these technologies, especially in a field where public sector budgets are constrained and the demand for data-driven, proactive maintenance is high. By combining hardware and software in an integrated offering, EyeVi aims to deliver a unified data-product that can feed digital twins, support planning processes, and provide a clearer, more actionable view of road conditions for engineers, municipal administrators, and contractors alike.

The digital twin concept: consolidating heterogeneous road data

A central theme in EyeVi’s narrative is the formalization of a digital twin for road infrastructure. The company contends that the most significant bottleneck in advancing road maintenance lies in overlaying data from disparate systems and surveyors into a single, comprehensive digital twin. This digital twin is envisioned as a dynamic, living representation of road networks that can inform planning, budgeting, and communications.

In EyeVi’s framing, the world of road data is inherently fragmented. One system may collect data about the different layers inside the road—such as base materials, asphalt or concrete composition, and drainage layers. A separate data collection effort might focus on potholes, including their size, depth, and frequency of appearance. A third data stream could capture the chemical content of the pavement, perhaps related to materials used in different seasons or under varying environmental conditions. The result is a mosaic of information that is difficult to reconcile, leading to gaps in understanding and inefficiencies in decision-making. EyeVi argues that the next phase of progress lies in automating the process of combining these layers into a single digital platform. This platform would not only amalgamate the data but also enable analyses that reveal how changes in traffic patterns, weather events, maintenance interventions, and construction practices interact to influence road quality over time.

The envisioned digital twin would serve multiple purposes. It would streamline planning by providing a unified source of truth about road conditions, enabling more accurate forecasts of when maintenance actions are required and which interventions would yield the best outcomes. It would improve communications among stakeholders by offering a common, data-driven baseline for discussions about priorities, budgets, and timelines. And it would support ongoing monitoring and assessment, allowing road authorities to observe how the road network responds to different variables in near real time and adjust strategies accordingly. The automation aspect is crucial: by reducing the manual burden of data integration and analysis, the digital twin could scale to cover vastly larger road networks at lower cost, thereby accelerating the adoption of proactive maintenance practices.

The practical implications of this approach are significant. A unified digital twin could enable agencies to simulate how new construction techniques or road formulations might affect durability and performance, which in turn informs procurement decisions and life-cycle cost analyses. By making data more accessible and interpretable, it could help prioritize maintenance and repairs in a way that optimizes resource allocation, reduces traffic disruption, and extends the life of road assets. EyeVi’s focus on automation and AI is designed to reduce the time between data collection and decision-making, transforming the traditional cycle of inspection, reporting, and action into a more seamless feedback loop that supports continuous improvement in road performance. In this sense, the digital twin is not just a data repository; it is a dynamic decision-support system that translates diverse observations into actionable insights.

As EyeVi pursues this digital twin vision, the company also emphasizes the broader potential impact on public infrastructure finance. By improving the quality and granularity of road data, digital twins can help quantify the effects of different construction techniques, materials, and maintenance regimes on road longevity and safety outcomes. This enhanced data fidelity could, in turn, inform prioritization schemes and investment decisions, potentially reducing the risk of underfunded projects and helping agencies allocate limited resources more efficiently. The ultimate promise of the digital twin is a virtuous cycle: better data leads to smarter decisions, which leads to better road performance, which then yields further cost savings and improved safety. EyeVi positions itself at the confluence of hardware-enabled data capture and cloud-based analytics, aiming to deliver a scalable platform capable of building and maintaining these digital twins across large and heterogeneous road networks.

The U.S. funding challenge and the promise of automation

The economic context surrounding EyeVi’s mission is rooted in the substantial funds allocated to road and bridge projects in the United States, alongside a substantial funding gap that persists in the face of aging infrastructure. In the United States alone, public authorities—spanning city, state, and federal highway agencies—spent a sizable amount on roads and bridges in past years, with estimates indicating a figure around $177 billion for the year 2017. This figure highlights the scale of investment involved in maintaining and expanding the nation’s road network, underscoring the opportunity for technologies that can improve efficiency and outcomes.

Beyond the annual expenditure, there is a looming backlog of underfunded needs that complicates strategic planning. Analysts estimate that the backlog sits at about $786 billion, a figure that reflects the gap between required investments to preserve current asset quality and the actual funding that has been available. Several structural factors contribute to this mismatch: inflationary pressures, shifts in fuel efficiency and consumption patterns, and a funding model based on gas taxes that has not fully kept pace with changing usage and vehicle efficiency. As a result, many road authorities face persistent pressures to stretch dollars further while maintaining safety and reliability.

In this context, EyeVi argues that more accurate and automated digital twins of roads could play a meaningful role in closing the funding gap. By providing clearer, more reliable data about how variations in construction techniques, road formulations, and maintenance strategies translate into road quality, the digital twin can enable better prioritization of interventions. When decisions about which projects to fund are driven by robust data and scenario analyses, agencies can allocate resources to maximize long-term performance and minimize unnecessary expenditures. Moreover, automating the data integration process reduces the labor and time required to produce up-to-date road intelligence, potentially lowering the total cost of ownership for digital twin systems and accelerating the deployment of targeted maintenance programs.

In this broader drive toward smarter asset management, national organizations are also pursuing cost savings through more efficient methods of pavement renewal. The American Association of State Highway and Transportation Officials (AASHTO) is reported to be in the early stages of rolling out a more cost-effective system for pavement renewal, with projections of tens of billions of dollars in savings through improved processes and data-driven decision-making. While the specifics of this system remain to be publicly described in detail, the promise lies in a more optimized allocation of resources, a reduction in lifecycle costs, and the streamlining of renewal activities that collectively contribute to better network performance and reliability.

Early results from one state agency illustrate the potential for reduced costs and traffic disruption. The Washington Department of Transportation (WDOT) has reported findings suggesting that a shift toward more efficient pavement renewal strategies could yield a 30 percent cost savings over the life of new pavement, coupled with a 50 percent reduction in construction-related traffic jams. While these numbers are early and likely contingent on the specifics of implementation, they demonstrate the magnitude of potential impact when data-driven processes are adopted at scale. If similar improvements could be replicated nationwide, the aggregated savings could reach tens of billions, providing resources to fund additional projects or to upgrade other components of the transportation system. The argument for digital twins and automated data capture grows stronger when framed in terms of broad, system-wide efficiency gains and the ability to deliver more predictable project outcomes.

Scaling the results from pilot or regional implementations to nationwide adoption represents a major challenge, but proponents argue that the automation and standardization at the heart of digital twins could unlock the capacity needed to manage larger portfolios of projects without sacrificing quality or safety. By incorporating automated processes for collecting data about road conditions, construction quality, and maintenance outcomes, agencies can create repeatable workflows that reduce variability, improve transparency, and facilitate coordination among multiple stakeholders. These capabilities are essential when managing the multifaceted lifecycle of road infrastructure—design, construction, operation, and renewal—across a dense and diverse network.

EyeVi’s approach to automating the process of capturing digital twins of road infrastructure as it is built and as it deteriorates holds particular relevance for a nation seeking to scale its infrastructure investments more efficiently. The company contends that advances in AI, computer vision, and data integration can help translate disparate datasets into unified, decision-ready insights. By enabling a more precise understanding of how road materials perform under different conditions and how various maintenance strategies affect long-term outcomes, EyeVi and its peers could contribute to a broader effort to improve asset management, reduce life-cycle costs, and optimize the allocation of funds to maximize public value.

Market landscape, competition, and the growth trajectory of AI-powered road monitoring

The market for AI-powered road monitoring and automated road inspection is gaining attention as governments and contractors seek to accelerate the digital transformation of road management. EyeVi positions itself within this evolving landscape, competing with other players that offer AI-enabled road inspection solutions. Notable companies in this space include Pavemetrics and RoadBotics, which are developing AI-driven road monitoring software as a service that can accelerate digital transformation for road management agencies. EyeVi’s strategy aligns with these peers by providing a combination of computer-vision hardware and cloud-based analytics that aim to streamline data capture and analysis for road infrastructure.

In addition to EyeVi and the named competitors, the broader ecosystem includes companies that crowdsource data from dashcam footage and vehicle telematics to gather more granular details about traffic patterns, road texture, and traction. Notable players in this adjacent space include Nexar, Tactical Mobility, and NIRA Dynamics. These organizations contribute to a larger trend of leveraging ubiquitous data sources from vehicles and on-road sensors to infer road conditions and driver risk in near real time. EyeVi’s differentiated proposition, however, centers on its integrated platform designed explicitly for road service surveyors, repair crews, and municipalities, combining data capture hardware with AI-driven analysis and digital twin capabilities tailored to road maintenance planning and operations.

Market research in the sector points to a growing total addressable market. Estimates cited in industry discussions suggest that the market for road inspection systems could expand significantly over a multi-year horizon, driven by demand for automated, scalable solutions that can reduce costs and improve safety. Projections indicate a ramp-up in market size from hundreds of millions to well over a billion dollars as adoption broadens across regions and government agencies embrace digital twins and AI-enabled workflows. EyeVi’s growth plans reflect this trajectory, with a stated goal to scale from tens of thousands of miles surveyed to around one million miles within a defined period. This expansion aligns with broader industry ambitions to digitize road data capture, standardize data formats, and enable interoperable platforms that can serve multiple agencies and contractors in a cohesive ecosystem.

The competitive landscape also features partnerships and collaborations that can influence EyeVi’s growth. The company’s seed funding and the profile of its investors highlight a cross-border interest in AI-driven infrastructure technologies, suggesting that EyeVi’s approach could attract strategic collaborations, pilots, or customer pilots across both European and North American markets. As digital twin technologies mature, there is an opportunity for EyeVi to integrate its hardware and software stack with complementary offerings from other players in data analytics, simulation, and asset management, expanding the potential for holistic road management solutions that span data collection, analysis, and decision support.

Growth plan: expanding coverage, automating digital twins, and scaling miles surveyed

A key objective for EyeVi is to broaden its coverage and capabilities while advancing the automation of digital twins for road infrastructure. The company has highlighted an aggressive plan to scale its service from surveilling approximately 20,000 miles last year to around 1 million miles in a shorter time frame. Achieving this leap would require not only product refinements but also a robust go-to-market strategy, partnerships with agencies and contractors, and the establishment of scalable deployment models that can support large districts and metropolitan areas in the United States and beyond.

Automating the data-capture and integration processes is central to this growth strategy. By reducing the manual effort required to collect, harmonize, and analyze data from multiple sources, EyeVi aims to shorten the cycle from data collection to actionable insights. This acceleration could translate into more rapid maintenance decisions, better asset management, and more predictable project outcomes. The platform’s potential to consolidate multiple data streams—ranging from structural details of road layers to pothole data and pavement chemistry—into a single, coherent digital twin could provide a powerful basis for optimization across the lifecycle of road networks.

The funding round that supported EyeVi’s initial push also signals a milestone in validating the company’s business model and technology approach. With backing from ff Venture Capital and a diverse group of investors, EyeVi has access to capital and networks that can facilitate product development, hiring, and strategic partnerships. The involvement of Estonian angels and prominent regional investors further strengthens EyeVi’s international appeal, reinforcing the notion that there is global interest in AI-driven infrastructure tools that can deliver measurable savings and improved performance.

As EyeVi moves forward, the company’s strategy will likely emphasize a combination of continued product innovation, pilot programs with municipalities, and expansion into U.S. markets where the demand for cost-effective, automated road data solutions is pronounced. The ability to demonstrate concrete cost savings, reliability, and scalability will be essential to securing multi-year contracts and broad adoption. In this environment, EyeVi’s digital twin platform could serve as a catalyst for modernization across road agencies by enabling standardized data collection, better interoperability between systems, and clearer insights into the relationships among construction practices, road formulations, and long-term durability.

The broader implications: from data capture to policy and planning

EyeVi’s work sits at the intersection of technology, infrastructure policy, and public budgeting. By enabling more accurate, automated road data capture and by delivering a robust digital twin of road networks, EyeVi is positioning itself to influence how agencies plan, allocate resources, and evaluate outcomes. The potential for improved data fidelity to drive better decisions could reshape procurement strategies, maintenance cycles, and the prioritization of capital projects. As agencies adopt digital twin platforms and AI-driven analytics, policy implications may include the standardization of data formats, the creation of shared data infrastructures, and the adoption of performance-based budgeting approaches that reward outcomes over inputs.

The use of digital twins in road management also carries societal benefits beyond budgetary efficiency. With more precise traffic models and maintenance planning, there is the potential for reduced traffic congestion during road work, improved safety through better anticipation of road deterioration, and more reliable travel times for commuters and goods transport. While these outcomes depend on successful implementation and scale, EyeVi’s approach aligns with a broader movement toward data-enabled governance, where technology augments the capacity of public agencies to manage critical assets with greater transparency and accountability.

EyeVi’s focus on automating digital twins could also accelerate the adoption of related technologies in adjacent areas of infrastructure, such as bridges, tunnels, and drainage systems. If the underlying data capture and AI tooling prove adaptable across asset classes, the platform could become part of a larger ecosystem of asset-management solutions that support lifecycle optimization, risk assessment, and resilience planning in the face of climate change and increasing demand for mobility. The potential ripple effects include new workflows for inspectors, engineers, and procurement teams, as well as opportunities for software-enabled optimization that complements traditional engineering methods.

In sum, EyeVi’s trajectory illustrates how a focused AI-driven data capture platform can begin to reverse long-standing inefficiencies in road maintenance. By offering a scalable solution that integrates diverse data streams into a single digital twin, EyeVi seeks to transform planning, execution, and outcomes across the road infrastructure lifecycle. The company’s progress will be watched by policymakers, industry observers, and municipal buyers who are seeking measurable improvements in efficiency, safety, and reliability as they navigate the complex economics of maintaining and renewing vital road networks.

Conclusion

EyeVi is pursuing a transformative path for road infrastructure management by integrating computer-vision hardware with AI-powered SaaS to automate the capture and analysis of road data. The company targets a significant reduction in the cost of road surveys—about one-hundredth of traditional manual methods—and aims to extend its platform into United States markets to support road operations and maintenance on a large scale. A central tenet of EyeVi’s strategy is the development of a comprehensive digital twin for road networks, designed to overlay data from multiple sources into a single, actionable platform that informs planning and communications. This digital twin concept addresses a key bottleneck in road maintenance: the challenge of merging diverse data streams into a cohesive representation of road conditions and performance.

EyeVi’s fundraising success, led by ff Venture Capital with participation from multiple investors, provides the capital and backing needed to push aggressive growth targets. The seed round, totaling approximately $2 million, signals strong investor confidence in the potential of AI-driven road monitoring and automation to deliver significant cost savings and operational improvements for public agencies and contractors. The broader market context reinforces the perceived value of EyeVi’s approach. Industry projections and pilot results from agencies like Washington DOT suggest meaningful cost reductions and reductions in traffic disruption can be realized through data-driven pavement renewal strategies and automation. The convergence of a large, aging road network, rising maintenance costs, and a growing appetite for data-centric decision-making creates a compelling opportunity for EyeVi and its peers to redefine how road infrastructure is surveyed, maintained, and renewed.

As EyeVi scales toward one million surveyed miles and beyond, the company’s emphasis on automating the integration of diverse road data into a unified digital twin will be critical to achieving sustainable, long-term impact. If successful, EyeVi’s platform could serve as a foundational tool in the modernization of road networks, enabling more efficient allocation of resources, better planning, and more predictable maintenance outcomes—ultimately contributing to safer, more reliable transportation systems for communities across the United States and globally.