The implementation of large-scale models

We transferred the customer service ticket processing to Claude 3.5 Sonnet, saving 21,000 euros in labor costs over three months.
This article is based on the real experiences of a European e-commerce startup with a team of 10 people and shares the implementation results of using Claude 3.5 Sonnet to handle multilingual customer service tickets: it saved 21,000 euros in labor costs over 3 months, and the ticket response time was reduced from 17 hours to 1.5 hours. It also discusses the pitfalls encountered in actual use, such as rule illusions and rate limiting, and provides clear applicable scenarios and tips for getting started.
2026-06-27 oneai 0 0
We increased the efficiency of handling customer after-sales tickets by a factor of 3 using Claude 3, but we ran into two problems that we shouldn’t have encountered.
This article is based on the real experiences of a Southeast Asian e-commerce SaaS team. It breaks down the actual benefits of Claude 3 in the context of after-sales ticket management, identifies the specific pitfalls encountered, clarifies the boundaries for which teams it is suitable or not, and provides practical recommendations that can be immediately implemented. This helps smaller and medium-sized teams quickly determine whether they need to integrate Claude 3 into their systems.
2026-06-27 oneai 0 0
Southeast Asian fresh food e-commerce companies use Claude for order quality inspection, saving 3 customer service positions, but they've encountered 2 fatal pitfalls.
This article is based on the practical experience of a fresh food e-commerce team of 20 people in Southeast Asia. It shares the actual benefits of using Claude for order quality inspection, the limitations encountered, as well as the pitfalls related to output formats. It also clarifies the scenarios in which Claude is suitable and not suitable for use, and provides practical recommendations for shadow testing and version selection that can be immediately implemented. This article is suitable for small and medium-sized teams with needs for processing repetitive text.
2026-06-27 oneai 0 0
We maximized the accuracy of logistics address matching using OpenAI o1, but encountered three unexpected issues.
This article is based on the practical experience of a European logistics startup with three employees. It breaks down the core capabilities of the OpenAI o1 inference model, the benefits of implementing it, as well as real-world challenges encountered. It provides clear guidelines on when the model is suitable for use and offers tips for beginners, helping small and medium-sized developers quickly determine whether o1 is suitable for their business scenarios.
2026-06-27 oneai 0 0
We used GPT-5.5 in the European e-commerce customer service system, which saved 62% of the manpower, but we encountered two fatal pitfalls.
This article is based on the real experiences of a Dutch small and medium-sized e-commerce team and provides a detailed explanation of the actual capabilities of GPT-5.5: the error rate for multilingual address recognition has been reduced to 0.7%, and costs have been decreased by 62%. It also shares practical challenges such as information completion illusions and high fine-tuning costs. The article offers clear guidelines on when to use GPT-5.5 and tips for getting started, making it suitable for cross-border business developers to refer to.
2026-06-27 oneai 0 0
We saved 32% on manpower by using GPT-4 to process millions of after-sales tickets. Avoid these three common mistakes.
This article is based on the real experiences of a European cross-national home appliance retail startup and shares the actual benefits of using GPT-4 in multi-language after-sales ticket scenarios, including a 32% reduction in labor costs and an improved accuracy in fault diagnosis. It also identifies three common pitfalls: out-of-control costs, rate limiting, and misjudgment of sensitive content, and provides clear use cases and tips for getting started.
2026-06-27 oneai 0 0

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The implementation of large-scale models

We transferred the customer service ticket processing to Claude 3.5 Sonnet, saving 21,000 euros in labor costs over three months.

We increased the efficiency of handling customer after-sales tickets by a factor of 3 using Claude 3, but we ran into two problems that we shouldn’t have encountered.

Southeast Asian fresh food e-commerce companies use Claude for order quality inspection, saving 3 customer service positions, but they've encountered 2 fatal pitfalls.

We maximized the accuracy of logistics address matching using OpenAI o1, but encountered three unexpected issues.

We used GPT-5.5 in the European e-commerce customer service system, which saved 62% of the manpower, but we encountered two fatal pitfalls.

We saved 32% on manpower by using GPT-4 to process millions of after-sales tickets. Avoid these three common mistakes.