UST is bringing Claude to physical AI
UST is bringing Claude to physical AI
UST 将 Claude 引入物理 AI 领域
Before a factory commits to manufacturing millions of chips, engineers stress-test the design in the fab. Before a product ships, a fault on the assembly line has to be caught before it becomes a recall. When AI does this kind of work, it’s called physical AI: intelligence built into the equipment and engineering processes that produce the things people use.
在工厂投入数百万颗芯片的生产之前，工程师们会在晶圆厂对设计进行压力测试。在产品出货之前，必须在装配线上的故障演变成召回事件前将其捕获。当人工智能执行此类工作时，它被称为“物理 AI”：即嵌入到生产人们所用物品的设备和工程流程中的智能。
We’re partnering with UST, a technology and engineering services company that builds and runs the engineering environments its clients depend on to get chips, cars, and connected devices to market. UST is putting Claude to work inside those environments, and training 20,000 of its engineers, architects, and consultants on Claude worldwide.
我们正在与 UST 合作，这是一家技术与工程服务公司，致力于构建和运营其客户所依赖的工程环境，以将芯片、汽车和联网设备推向市场。UST 正在将 Claude 应用于这些环境中，并为其全球 20,000 名工程师、架构师和顾问提供 Claude 培训。
How UST is putting Claude into the production processes behind physical products
UST 如何将 Claude 应用于物理产品的生产流程
UST works alongside semiconductor, automotive, manufacturing, telecom, embedded, and IoT companies. It builds the systems those companies use to verify their designs, validate their chips, run their factories, and service their products once they’re out in the world.
UST 与半导体、汽车、制造、电信、嵌入式和物联网领域的公司并肩工作。它构建了这些公司用于验证设计、核实芯片、运营工厂以及在产品投放市场后进行维护的系统。
These are long, multi-step processes where an early mistake gets more expensive with every step that follows. A design flaw caught during verification costs an engineer an afternoon; the same flaw caught after a factory has committed to manufacturing costs a production run.
这些都是漫长且多步骤的流程，早期的错误在随后的每一步中都会变得更加昂贵。在验证阶段发现的设计缺陷只会让工程师浪费一个下午；而如果同样的缺陷在工厂投入生产后才被发现，则可能导致整批生产报废。
UST is bringing Claude into this work. Claude Code reads the schematics and pinouts an engineer works from, then writes and runs the tests that check the design. It carries that task across many steps, holding the context of a design through hours-long tasks. UST is aiming to catch design flaws earlier, speed up chip validation, and bring hardware and software together in a single system.
UST 正在将 Claude 引入这项工作中。Claude Code 可以读取工程师使用的原理图和引脚分配，然后编写并运行测试来检查设计。它能跨越多个步骤执行任务，并在长达数小时的任务中保持对设计上下文的理解。UST 的目标是更早地发现设计缺陷，加快芯片验证速度，并将硬件和软件整合到一个系统中。
The clearest example is a UST platform called iDEC, which its engineers use to validate hardware and silicon before it goes to production. Validation is the work of proving a chip actually behaves the way its designers intended, and it’s arduous: engineers write test scripts by hand, run them, read the results, and repeat the cycle many times over. UST reports that iDEC’s closed-loop pipeline, reads hardware designs, generates and runs regression tests, and compares live equipment data against its digital twin to flag issues early, already cuts validation cycle times by 50 to 70%, condensing standard four-day turnarounds into 48 hours.
最典型的例子是 UST 名为 iDEC 的平台，其工程师使用该平台在硬件和芯片投入生产前进行验证。验证工作旨在证明芯片的行为确实符合设计者的意图，过程非常艰巨：工程师需要手动编写测试脚本、运行脚本、读取结果，并多次重复这一循环。据 UST 报告，iDEC 的闭环流水线能够读取硬件设计、生成并运行回归测试，并将实时设备数据与数字孪生进行对比以尽早标记问题，这已将验证周期缩短了 50% 到 70%，将标准的四天周转时间压缩到了 48 小时。
UST is now integrating Claude into that pipeline as its reasoning layer. Claude Code reads chip pinouts and hardware schematics directly, then writes and runs regression tests—the checks that confirm a change to a design didn’t cause an unintended downstream effect—which engineers used to script by hand. Claude also compares the live data from real equipment against its digital twin—the software model of how that hardware is supposed to behave—and flags firmware regressions and signal-integrity faults. UST’s goal is to make its pipeline even faster, with less hand scripting and earlier fault detection, and no new tools for engineers to learn.
UST 现在正将 Claude 集成到该流水线中作为其推理层。Claude Code 直接读取芯片引脚分配和硬件原理图，然后编写并运行回归测试（即确认设计变更未引起意外下游影响的检查），而这些工作过去通常由工程师手动编写脚本完成。Claude 还会将来自真实设备的实时数据与数字孪生（即硬件预期行为的软件模型）进行对比，并标记固件回归和信号完整性故障。UST 的目标是使其流水线运行得更快，减少手动脚本编写，实现更早的故障检测，且无需工程师学习任何新工具。
“Our alliance with Anthropic reflects UST’s unwavering commitment to helping clients navigate the AI landscape with confidence and achieve meaningful business outcomes. By combining the capabilities of Claude with UST’s engineering, industry knowledge, and delivery expertise, we are bringing to market industry-specific platforms and digital and engineering solutions that improve productivity, accelerate business outcomes, and help clients operationalize AI-led decisions in a safe and secure environment,” said Krishna Sudheendra, Chief Executive Officer, UST.
“我们与 Anthropic 的联盟反映了 UST 的坚定承诺，即帮助客户自信地驾驭 AI 领域并实现有意义的业务成果。通过将 Claude 的能力与 UST 的工程、行业知识和交付专长相结合，我们正在向市场推出特定行业的平台以及数字和工程解决方案，这些方案能够提高生产力、加速业务成果，并帮助客户在安全可靠的环境中实现 AI 主导决策的落地，”UST 首席执行官 Krishna Sudheendra 表示。
“UST helps the world’s banks, telecoms, and manufacturers put new technology to work. They’re proving Claude inside their own engineering first, and training 20,000 of their own people on it, before bringing it into the systems they build and run for clients,” said Paul Smith, Chief Commercial Officer, Anthropic.
“UST 帮助全球的银行、电信公司和制造商应用新技术。他们在将 Claude 引入为客户构建和运营的系统之前，先在自己的工程实践中验证 Claude，并对其 20,000 名员工进行了培训，”Anthropic 首席商务官 Paul Smith 说道。
How UST is putting Claude into healthcare, telecom, and banking systems
UST 如何将 Claude 应用于医疗、电信和银行系统
UST is bringing Claude into three other platforms it operates for clients: UST 正在将其运营的另外三个客户平台引入 Claude：
In healthcare, insurers and providers use UST CarePath to run member services, care management, and claims. Claude connects CarePath directly to its underlying claims and care systems, and turns scattered health data into clear next steps for care teams. Every recommended action routes to a person for approval before it reaches a member, and it stays inside the data controls healthcare requires.
在医疗保健领域，保险公司和医疗服务提供商使用 UST CarePath 来管理会员服务、护理管理和理赔。Claude 将 CarePath 直接连接到其底层的理赔和护理系统，并将分散的健康数据转化为护理团队清晰的后续步骤。每一项建议的操作在到达会员之前都会先提交给相关人员审批，并始终处于医疗保健行业要求的受控数据范围内。
In telecom, UST IntelliOps runs network operations. To keep networks up and running, teams work through the alerts that spot problems and outages, but this is a time-consuming process. Claude now helps operators spot service issues, predict failures in the radio access network (the towers and antennas that connect phones to the network), and shorten outages through response workflows, which a person still approves. For the teams watching the alerts, that means less time separating real problems from noise.
在电信领域，UST IntelliOps 负责运营网络。为了保持网络正常运行，团队需要处理各种警报以发现问题和中断，但这非常耗时。现在，Claude 帮助运营商发现服务问题，预测无线接入网（连接手机与网络的基站和天线）的故障，并通过仍需人工审批的响应工作流缩短中断时间。对于监控警报的团队来说，这意味着他们可以减少将真实问题与干扰信息区分开来所花费的时间。
In banking, most mid-sized institutions still run on core systems old enough that the ledger updates once a night rather than in real time. Banks often license these systems rather than own them, so every new product or integration means months of waiting for a vendor to make the change. UST FinX helps banks modernize progressively by solving immediate business and operational challenges while reducing the dependency on disruptive, high-risk transformation programs. FinX will use Claude to embed AI agents directly into bank workflows and processes, supporting both operations teams and customers through intelligent case handling, servicing automation, knowledge retrieval, workflow assistance, and decision support.
在银行业，大多数中型机构仍在运行陈旧的核心系统，其账本更新频率为每晚一次而非实时。银行通常是授权使用而非拥有这些系统，因此每项新产品或集成都意味着要等待供应商数月才能完成变更。UST FinX 通过解决当前的业务和运营挑战，同时减少对破坏性、高风险转型项目的依赖，帮助银行逐步实现现代化。FinX 将利用 Claude 将 AI 代理直接嵌入到银行的工作流和流程中，通过智能案例处理、服务自动化、知识检索、工作流辅助和决策支持，为运营团队和客户提供支持。
A partnership in AI adoption and training
AI 采用与培训方面的合作伙伴关系
UST is committing to train 20,000 of its associates on Claude worldwide, including engineers, architects, consultants, industry specialists, and forward-deployed engineers, who sit alongside client teams. It’s also building specialized teams.
UST 承诺为其全球 20,000 名员工提供 Claude 培训，包括工程师、架构师、顾问、行业专家以及与客户团队并肩工作的驻场工程师。它还在组建专门的团队。