We go into the plant, work out which problem is worth solving first, and build the answer into a system. Five months in, six applications run in production and engineers use them every day.我们进厂，和客户一起判断哪个问题最值得先解决，再把解法做成系统。五个月后，六个应用跑在生产环境里，工程师每天在用。
Five months in · IN PRODUCTION五个月之后 · IN PRODUCTION
In Production已经在运行
Built with a listed new-energy-materials manufacturer, running in their production environment.与一家上市新能源材料制造企业共建，运行在他们的生产环境里。
Process specs, equipment manuals, inspection standards and industry norms, asked in plain language.工艺规范、设备手册、质检标准与行业规范，用大白话问。
Connected to the customer’s ERP, so staff ask about stock, orders and prices directly, and answers carry their data source.接上客户的 ERP，库存、订单、价格直接问，答案带数据来源。
Findings from yield work go back into the console, callable from the line.良率攻坚的结论回流到控制台，产线上可以直接调用。
Defect recognition and grading on the production line.产线上的缺陷识别与判级。
Literature and prior work organized into something a researcher can actually navigate before committing to experiments.把文献与既有工作整理成研究员真能用的形态，在投入实验之前先看清版图。
Recurring plant problems, their history, and what was tried — kept in one place.反复出现的问题、它的历史、试过什么，收在一处。
How We Work做法
The first month goes into production, quality, procurement and R&D — listening until it is clear which problem is worth solving first.第一个月花在生产、质控、采购、研发各条线上，听到能判断出哪个问题最值得先解决为止。
Around 20,000 lines of domain knowledge organized and ingested, running locally — data never leaves the plant.约两万行行业知识整理入库，本地运行，数据不出厂区。
This one is a gate, not a goal: material that cannot be traced back stays out of the knowledge base.这条是闸不是目标：追不回出处的材料，就留在库外。
How It Scales技术底座
The knowledge engine is domain-agnostic — ingest, represent, serve, evaluate.知识引擎与具体领域无关，做四件事：摄取、表示、服务、评估。
Adding an industry means adding a knowledge pack; the engine stays as it is.新增一个行业，是加一份知识包，引擎照旧。