ECARX Secures Non-Automotive Customer for its Lidar Solution, Expanding into the High-Growth Robotics Market
Partnership with leading robotic lawn mower developer marks strategic diversification beyond automotive intelligence sector
SHANGHAI, June 25, 2025 (GLOBE NEWSWIRE) -- ECARX Holdings Inc. (Nasdaq: ECX) ('ECARX' or the 'Company'), a global mobility tech provider, today announced it has signed a partnership agreement with a leading global developer of robotic lawn mowers to integrate ECARX's cutting-edge lidar solution. This partnership represents a significant milestone in ECARX's strategy to diversify the application of its solutions beyond its core automotive intelligence segment and capitalize on opportunities in the rapidly growing robotics and AI applications markets, with global mass production planned for 2026.
The robotics market represents a natural extension of ECARX's sensor technology expertise, enabling the Company to monetize its automotive R&D investments across new high-growth sectors. The deep integration of artificial intelligence and robotics is accelerating, driven by increased investment from global tech leaders. This evolution is propelling the shift from concept to real-world application, paving the way for a scalable industry with vast market potential.
ECARX's proprietary solid-state 3D short-range lidar provides the high-precision environmental perception essential for autonomous robot operations. Operating at a 905nm wavelength with no mechanical components, ECARX's lidar ensures superior reliability and performance. The lidar incorporates a customized large-array addressing VCSEL light source with 60-meter detection range and a high-resolution SPAD sensor for precise environmental mapping – critical for advanced obstacle avoidance systems used in robotic navigation, object manipulation, and human-machine collaboration.
Similar to ECARX's successful automotive partnerships with 18 automakers across 28 global brands, the Company is now extending its proven ecosystem approach to include robotics applications. The Company's ability to integrate advanced sensor technologies across multiple platforms demonstrates its comprehensive approach to intelligent systems development and how they can be scaled efficiently. This strategy validates ECARX's technology versatility while opening new addressable markets beyond its automotive customers.
Ziyu Shen, Chairman and CEO of ECARX, commented, 'This partnership validates the application of our cutting-edge technologies and extends it far beyond the automotive market. We're excited to unlock new growth opportunities in robotics while demonstrating the versatility and commercial potential of our advanced sensor solutions. Moving forward, we plan to expand our presence in the robotics and AI sectors by actively collaborating with more industry partners to jointly advance this transformative technology. As application scenarios continue to diversify, we are committed to leveraging our lidar solutions and innovation capabilities to contribute meaningfully to the development of intelligent robotics and help shape the future of embodied intelligence.'
About ECARX
ECARX (Nasdaq: ECX) is a global automotive technology provider with capabilities to deliver turnkey solutions for next-generation smart vehicles, from the system on a chip (SoC), to central computing platforms, and software. As automakers develop new electric vehicle architectures from the ground up, ECARX is developing full-stack solutions to enhance the user experience, while reducing complexity and cost.
Founded in 2017 and listed on the Nasdaq in 2022, ECARX now has around 1,800 employees based in 12 major locations in China, UK, USA, Sweden and Germany. The co-founders are two automotive entrepreneurs, Chairman and CEO Ziyu Shen, and Eric Li (Li Shufu), who is also the founder and chairman of Zhejiang Geely Holding Group — with ownership interests in global brands including Lotus, Lynk & Co, Geely Galaxy, Polestar, smart, and Volvo Cars. ECARX also works with other well-known automakers, including Volkswagen Group, FAW Group and Dongfeng Peugeot-Citroën. To date, ECARX products can be found in over 8.7 million vehicles worldwide.
Forward-Looking Statements
This release contains statements that are forward-looking statements within the meaning of the U.S. Private Securities Litigation Reform Act of 1995. These statements are based on management's beliefs and expectations as well as on assumptions made by and data currently available to management, appear in a number of places throughout this document and include statements regarding, amongst other things, results of operations, financial condition, liquidity, prospects, growth, strategies and the industry in which we operate. The use of words 'expects', 'intends', 'anticipates', 'estimates', 'predicts', 'believes', 'should', 'potential', 'may', 'preliminary', 'forecast', 'objective', 'plan', or 'target', and other similar expressions are intended to identify forward-looking statements. These forward-looking statements are not guarantees of future performance and are subject to a number of risks and uncertainties that could cause actual results to differ materially, including, but not limited to, statements regarding our intentions, beliefs or current expectations concerning, among other things, results of operations, financial condition, liquidity, prospects, growth, strategies, future market conditions or economic performance and developments in the capital and credit markets and expected future financial performance, and the markets in which we operate.
For a discussion of these and other risks and uncertainties that could cause actual results to differ materially from those expressed in any forward-looking statement, see ECARX's filings with the U.S. Securities and Exchange Commission. ECARX undertakes no obligation to update or revise forward-looking statements to reflect subsequent events or circumstances, except as required by applicable law.
Investor Contacts:ir@ecarxgroup.com
Media Contacts: ecarx@christensencomms.com
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
23 minutes ago
- Forbes
AI In E-Commerce: Are Brands And Customers Aligned?
Rytis Lauris is the cofounder and CEO of Omnisend, a marketing automation platform built for e-commerce. I don't think there is a single industry AI hasn't touched, and e-commerce is certainly no exception. Chatbots, product recommendations, segment creation and churn prediction have been well underway for some time, but these tools are only the beginning. It seems like every day we read stories of another brand implementing new and improved AI on their site. However, while brands see the value and promise in using AI to revolutionize the customer experience, do consumers want it, and if so, for what purposes? What Online Shoppers Want From AI While times will surely change, currently, many consumers want AI to improve their shopping experiences, not transform them. A consumer survey from my company learned that the AI-powered features shoppers found most useful were improved product recommendations and faster product discovery. Nearly half also said they want to see improvements in customer service. However, product recommendations, whether on-site or in marketing emails, aren't always relevant, much to the dismay of shoppers. This is an opportunity for brands to use AI to give shoppers what they want. The more relevant the recommendations, the easier it is to find a product, leading to a better chance of capturing a sale. For example, take Muse by Wayfair, where users can type a description of what they're looking for (e.g., "modern chic dining room with hints of lime") and find design inspiration. Upon clicking on the 'muse' they like, shoppers can click on the Wayfair products pictured in the image. The challenge for brands with customer service is that many chatbots, though improving, are clunky and often frustrating. This needs to change. For more complex inquiries, there needs to be a seamless escalation procedure that directs to human representatives without losing the chat history or background from the customer. If chatbots or other AI customer service tools can't provide a more intuitive level of support, it creates friction and becomes a detriment to the business. This can't happen. Agentic AI, where autonomous decisions are made, is a rapidly advancing area for e-commerce brands. Maybe the most newsworthy example is Amazon's Buy For Me feature, which allows users to discover products through the Amazon site that are not sold on Amazon and then complete the purchase directly with the off-site retailer on their behalf. Amazon isn't alone in using agentic AI to drive purchases. Visa and Mastercard got into the fray with tools that can make purchases on people's behalf. Admittedly, we are at the early stages of this type of use case for agentic AI, and it's going to be interesting to see where it leads. But right now, most consumers aren't quite ready. In my view, the current behavior for most shoppers is to verify all details, from size and color to shipping information, before clicking 'complete purchase.' Trusting AI to select the right size, color and other variables feels like a large behavioral and trust issue that needs to be overcome. Agentic AI holds a lot of promise for e-commerce brands on the customer-facing and operational sides of the business. It can be used to make product recommendations, sift through product reviews, solve customer service inquiries and track and find lower prices. Operationally, it can be used to track and forecast inventory, analyze data, personalize marketing campaigns and detect fraud. What Brands Can Do Now Consumers' hesitation to use AI for more than basic improvements to their shopping experience isn't irrational. The online purchase process is mostly the same as it was 20 years ago: search, click, browse, add to cart and check out. Expecting consumers to abandon this instinctive behavior overnight is an impossible ask, especially when it comes to making purchases for them. Trust in AI needs to be established and built through use and consistency. But like ecommerce's early days, it's going to be an evolution—one step at a time. If AI applications are consistently useful for shoppers, they'll trust and adopt them. If they're not, they won't. Brands can look to AI to help in various ways, but what they can't lose sight of is that, within this, shopping is often enjoyable for people. Leaving this to programmatic tools removes the thrill of the hunt to find the perfect item. Striking this balance will be a challenge. As such, e-commerce brands should adopt AI thoughtfully and use it to put consumers first. While experimenting with new tools is fine, the goal should be to give shoppers better experiences, whether it's support, product discovery or personalization. If brands can't execute the fundamentals and instead create friction, asking them to change their instinctive shopping behaviors will be a pipe dream. The two 'simplest' ways brands can begin building consumers' trust in AI is through chatbots and product search. Chatbots have come a long way in the past several years, moving from a tool that provides help article links to one that can accurately provide answers and triage customer concerns. By refining customer service chatbots to handle customer queries accurately, consumers can realize their value and trust them further. This trust in one tool gives them a reason to trust the next one. On-site search is another area where brands can use AI to support the customer experience. As search moves from keyword matching to an intuitive prediction model, shoppers' ability to discover additional products and have an expanded, non-linear shopping experience improves. A non-linear shopping experience creates a more enjoyable one, similar to the surprise-and-delight experience of in-store shopping. If AI-powered search results stay relevant and helpful, shoppers are more likely to see AI as a helpful asset vs. an annoyance. The Takeaway Is AI in e-commerce going anywhere? Absolutely not. Will it transform the way online shopping is done? Probably. Are consumers clamoring for radical changes instead of everyday improvements? Not yet. It's OK to dream big, and I'm excited for what's to come, but don't lose sight of what got you here: your customers. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
23 minutes ago
- Forbes
When AI, Energy Demands And Capital Costs Collide
Charles Yeomans is the chairman and founder of Atombeam. In a testimony to Congress in April 2025, Google's former CEO Eric Schmidt addressed not only the future of AI but also the dramatic power consumption associated with it. Although the dramatic increase in energy used by data centers associated with AI workloads is well known—and my last article touched on this crucially important issue—the sheer scale of the challenge is becoming clearer. Uncharted Territory More specifically, Schmidt noted that data center energy consumption could quickly increase from 3% of the power generated today to 99% of that current value, with data centers requiring 67 more gigawatts of power by 2030. He also subsequently put that point in perspective by pointing out that the average nuclear power plant creates about 1 gigawatt of power, even as technology providers plan for 10-gigawatt data centers. Although those figures are subject to some debate—the U.S. Department of Energy, for example, noted that data centers consumed about '4.4% of total electricity in 2023 and is expected to consume from 6.7 to 12% of total U.S. electricity by 2028'—one thing is certain: The proliferation of AI and the more powerful computational capabilities fast-evolving use cases, algorithms and models demand brings with it a power generation challenge of unprecedented proportions. Not surprisingly, to date, nuclear power is seen as the most viable, although still arguably insufficient, answer—a reality made clear by the nuclear ambitions of the largest technology providers. In early May 2025, Elementl Power announced its agreement with Google to build 'three sites for advanced reactors,' as reported by CNBC. Notably, though, Elementl Power, founded in 2022, has yet to build any nuclear sites. We're in uncharted territory. Coming To Terms Clearly, questions abound not just about national security—something Schmidt also stressed—and the feasibility not just of how data centers will be powered but also how quickly they can be built to keep up with AI. This is a point I also made in my last article while pointing to October 2024 research from McKinsey analysts, who found that 'to avoid a deficit, at least twice the data center capacity built since 2000 would have to be built in less than a quarter of the time.' One point, though, has yet to get the attention it fully deserves as we collectively come to terms with the dramatic demands associated with AI. That point is the sheer scale of the capital needed to facilitate the building boom required to create the infrastructure AI demands. But that, too—like the scale of the power consumption Schmidt brought into focus—is beginning to become more clear as April 2024 research, also from McKinsey analysts, reveals. That study predicts that by '2030, data centers are projected to require $6.7 trillion worldwide to keep pace with the demand for compute power.' Consider for a moment the awe-inspiring reality of that number. In 2025, total federal outlays in the U.S. are predicted to reach $7.0 trillion, or 23.3% of the GDP. Viewed differently, that same amount represents nearly 38% of China's GDP of $17.79 trillion, according to the World Bank. And we're talking just about the creation of new data centers. Cost Considerations Of course, data centers are obviously but one piece of the networks needed to use, move and store data associated with AI-powered use cases and workloads. The power consumption and capital costs associated with the infrastructure AI demands also apply to the pipes that connect everything, from the wired infrastructure to the cellular and satellite networks that pull it all together and enable AI outputs to be used in real-world applications. All of those critical components of AI infrastructure require massive capital investments and expenditures. Subsea cable, for example, costs $40,000 per mile, or between $200 million and $250 million for a transatlantic connection. Despite the increased economy, launching satellites remains a costly endeavor, with outlays being highly variable and impacted by numerous factors, from the orbit itself—geostationary Earth orbit costs more than low Earth orbit—to the weight of the satellite, the launch vehicle involved and, of course, the cost of making the satellite itself. Questions also abound about how costs will evolve and how quickly in light of significant advancements in space delivery technologies. Cellular networks are no less complex and also require significant capital investments. For example, in just one deal with U.S. Cellular in 2024, Verizon confirmed earmarking $1 billion for the purchase of additional spectrum alone. The Legacy Problem And then there's the issue of legacy communications and industrial networks that remain in use across many industries. These systems will need to be upgraded to handle large AI workloads if commercial applications of AI are to progress as envisioned. This includes the supervisory control and data acquisition systems (SCADA) that remain prevalent in the utility sector and across industries like manufacturing, with many now two and three decades old. But the issue of legacy systems isn't confined to any one sector. Link 16, the U.S. military's most-used digital datalink, is now in its fourth decade of use. Focusing On The Data Clearly, given these factors, the costs of the modernization and expansion efforts AI demands aren't feasible as viewed currently, particularly in a time when public-sector and private-sector investments are contracting, not growing. Nor is it realistic to believe we collectively have the people and businesses needed to keep pace with AI's rapid expansion and building demands. That's why we must, now more than ever, be pragmatic not only about our response to the investment AI calls for but also the kinds of AI data we use and how we manage, store, share and use that information. It's time to focus on data itself. Does it need to be this big? Can we make it more efficient and reduce the strain on resources? Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Business Insider
24 minutes ago
- Business Insider
Synthflow AI is bringing 'conversational' voice agents to call centers. Read the pitch deck that it used to raise $20 million.
Synthflow, a startup that has built AI voice agents that can hold "natural-sounding" phone conversations with customers, has raised $20 million in funding. The Berlin-based startup's platform lets enterprise customers create and deploy AI voice agents without coding expertise. Companies can customize those agents to handle simple inquiries, customer support, and follow-up with actions like appointment scheduling. "You can think of Synthflow as a voice AI platform on a no-code basis that allows businesses to very easily and quickly build and deploy voice AI agents," Hakob Astabatsyan, cofounder of Synthflow, told Business Insider. Astabatsyan said his startup's tech can replace traditional automated call menus, in which users press buttons to select options. Instead, Synthflow's technology is built on large language models that can have "real-time contextual conversations," Astabatsyan said. In a demo video seen by BI, an AI agent answers a call from a customer seeking a car insurance quote. The AI agent asks for the customer's details in a relaxed, conversational tone before transferring to a human operator. Astabatsyan said the AI agent's responses come in under 400 milliseconds, which matches the human brain's processing speed for conversations. Astabatsyan founded Synthflow in 2023 with Albert Astabatsyan and Sassun Mirzakhan-Saky. The startup says it now has over 1,000 enterprise clients, often in call centers, in areas such as finance, healthcare, and education. It also offers its tech on a white-label basis and can integrate into a company's existing telephony system. Voice has become a key battleground among AI startups, with companies like ElevenLabs and PolyAI also raising millions in capital to develop AI for call centers. Astabatsyan said that fundraising for AI startups is "definitely not easy" because the expectations are "very, very high." He added that investors expect AI startups to grow "two times faster" than benchmarks from a few years ago. Synthflow's Series A round, which was led by Accel with participation from Atlantic Labs and Singular, brings the startup's total funding to $30 million. Synthflow plans to use the fresh funding to open a new US office and invest in developing its agent platform. BI got a look at the 10-slide pitch deck it used to secure the fresh funding. Synthflow AI Synthflow AI Synthflow AI Synthflow AI Synthflow AI Synthflow AI Synthflow AI Synthflow AI Synthflow AI Synthflow AI
