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ELI care
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ELI care

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Meta Mobility

One-Line Product Definition

Predictive solution for electric mobility anomalies. This is a predictive safety system that prevents major accidents by preemptively capturing hidden warning signs (e.g., battery thermal runaway or sudden acceleration malfunction) of electrified vehicles such as electric cars and autonomous vehicles through ultra-high-speed electrical signal analysis and AI. Embedded in various vehicle platforms, it captures invisible microcurrent patterns at 20ns intervals and analyzes them in real-time with AI, providing drivers and control systems with early warnings of pre-failure anomalies missed by conventional diagnostics.

Problem Definition

In electrified mobility such as electric vehicles, UAM (urban air mobility), and ships, fatal accidents such as battery thermal runaway and electronic control unit errors can occur, but it is currently difficult to identify imminent accident signs in real-time with existing diagnostic systems such as BMS.

Existing methods have limitations in sensor resolution and speed, so they cannot detect subtle analog electrical patterns and are mostly recognized after a problem has occurred. As a result, major accidents such as electric vehicle fires and sudden acceleration are not prevented, and the situation is dealt with after they occur.

In the age of autonomous vehicles, to ensure safety,"invisible signals that cause accidents"the need for a solution that can detect and take preemptive measures has emerged.

Key Differentiators

ELI care is innovative in that it combines ultra-high-speed edge sensing hardware and real-time AI analysis to proactively capture"invisible electrical anomalies"that existing vehicle diagnostic systems miss.

A 20-nanosecond response sensor (50 MS/s sampling) is connected to the vehicle's electronic control circuit to capture anomalies in minute current/voltage waveforms, and onboard AI instantly analyzes them to warn of potential risks several minutes to hours in advance. For example, it detects early signs of battery thermal runaway or sudden acceleration/deceleration malfunction patterns before an accident occurs and notifies the vehicle control system and user, thereby addressing problems that would otherwise only be known after they occur.

It is provided in the form of a multi-brand, multi-vehicle integrated kit and can be installed regardless of vehicle type, and is continuously improved with cloud-linked big data.

Thanks to this differentiation,"real-time analysis of invisible anomalies through the convergence of AI and mobility to prevent accidents"has been recognized for its innovation.

Key Adopters

The direct customers of this solution are likely to be electric vehicle and mobility operators and finished vehicle manufacturers. Automakers can embed this technology in their vehicles to improve safety, andmobility service companies (autonomous taxi operators, etc.)can also install it on their vehicles to prevent accidents.

In addition, organizations that manage large fleets of electric vehicles (B2B) – for example, logistics vehicle operators, bus companies, etc. – can adopt it in the form of a subscription service and use it for vehicle maintenance.

It can also be expanded to insurance-linked safety services ormaintenance subscription services (B2C). Initially, B2B contracts with automotive OEMs and B2B2C models for fleet operators are expected to be the main forms.

Scalability

This technology can be extended to all electrified systems with batteries and electronic control units. It has already been evaluated as applicable as a universal platform to other electrified systems such as urban air mobility (UAM), marine electric vessels, and industrial robots.

Therefore, there is great potential for expansion beyond automobiles to drones, eVTOLs, electric wheelchairs, energy storage systems (ESS), etc. It is not dependent on a specific country or environment, and overseas market entry is possible if it is designed to comply with the safety standards of the global automotive industry.

However, as it is an in-vehicle system, there are hurdles to overcome in passing automobile certification and safety regulations in each country. As the adoption of electric vehicles spreads, the market size will increase exponentially, and it can be linked to OTA diagnostics, vehicle data platforms, etc. to develop into a new service ecosystem.

Judges' Evaluation

With this CES Innovation Award,the vision of Metamobility as "the first step towards a predictable safety society"has been strengthened.

The CTA judging panel commented that *"the innovation of preventing accidents in advance by analyzing invisible electrical anomalies in real-time through the convergence of AI and mobility was outstanding,"* and it is attracting attention in the industry as an essential safety technology in the era of electrification.

In terms of technological completeness, it is currently in the demonstration stage with real vehicle PoC in progress with the Korea Automotive Technology Institute, AWS, etc., and it is known that projects are being discussed with some global automakers.

Market expectations are positive due to increased interest in safety due to issues such as electric vehicle fires, and while it has shown leading technology as a startup, there remain tasks for collaboration/standardization with large companies in the future.

Overall, it has been recognized as a solution that solves the blind spots of vehicle safety that have been underestimated and has achieved the CES Innovation Award (Human Security category).

Analyst Insights

⚠️ Impressive technology but market uncertainty – Although highly innovative as an AI-based vehicle safety prediction technology, there are many hurdles to overcome until adoption as an automotive industry standard and commercialization, so it remains to be seen whether it will successfully establish itself in the market.

The award list data is based on the official CES 2026 website, and detailed analysis content is produced by USLab.ai. For content modification requests or inquiries, please contact contact@uslab.ai. Free to use with source attribution (USLab.ai) (CC BY)

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