Industrial Manufacturing
Automotive OEMs & Suppliers
BYD, Geely, Changan, SAIC, CATL and more
Research and aftermarket agents: test-report parsing, ticket attribution, automated retrospectives — a closed loop.
- Typical Customers
- BYD, Geely, Changan, SAIC, CATL and more
- Sector
- Industrial Manufacturing
- Capabilities
- Research AgentTicket AgentClosed Loop
Typical customers
BYD, Geely, Great Wall, Changan, SAIC, GAC, Dongfeng, CATL, Fuyao Glass and Tuopu Group.
Pain points
bulky test reports analyzed by hand; massive aftermarket tickets manually classified and attributed; R&D, production and aftermarket data siloed; scarce standardized AI tools with high customization costs.
- 研发测试报告、试验数据文档庞大，人工梳理分析效率低。
- 售后故障工单海量，分类、归因、复盘全部人工处理。
- 研发、生产、售后数据割裂，无法形成闭环分析。
- Writing quality improvement reports and after-sales review reports takes time.
- 行业标准化 AI 工具稀缺，定制成本高。
Solution
a dual research-and-aftermarket agent system — smart test-report parsing and issue extraction, automatic ticket triage with fault attribution, generated quality-correction and monthly retrospective reports, and data barriers broken for a closed loop. End-to-end delivery: supply-chain customer expansion and computing delivery; koolda provides the standardized scenarios and the ticket/research skill library.
- Automotive industry research report+after-sales dual intelligent agent system.
- Intelligent analysis of test reports, classification of experimental data, and intelligent extraction of problems.
- 售后工单自动分拣、故障归因、高频问题统计。
- 质量整改方案、月度复盘报告自动生成。
- 打通研产售后数据壁垒，实现业务闭环赋能。
- One stop delivery guarantee: full process support for customer expansion, computing power delivery, and project implementation in the automotive industry chain.
- koolda 提供汽车行业标准化 AI 场景与工单、研报处理技能库。
Automotive Manufacturing Solution FAQs
What if the test report is too thick and cannot be manually analyzed?
The research report intelligent agent automatically analyzes the test report, extracts problem points, and generates a stack of reports to draw conclusions in minutes.
What should I do if there are a large number of after-sales work orders and the attribution cannot be supported by manual labor?
After sales intelligent agents automatically sort work orders and attribute faults, and generate quality improvement and monthly review reports automatically.
Can the R&D, production, and after-sales data be connected?
Can. Dual intelligent agents break down barriers to research, production, and after-sales data, forming a closed-loop review.
Don't see your industry?
Every solution can be tailored on the koolda platform — tell us about your business scenario and we will plan the delivery path together.