Using the OpenAI API to validate European logistics addresses: We saved 32% on labor costs and also avoided two critical mistakes.

Last week, the Black Week promotional campaign just ended. During our technical team's review meeting, we found that the success rate of automatic address validation this year has increased by 41% compared to last year. As a result, we only need to keep one of the three part-time employees who were previously dedicated to handling exceptional addresses; the other two can be laid off this year.

But no one wants to talk about the bug with the 429 error that occurred last month – during the busiest 3 hours on the first day of the big promotion,13% of address resolution requests are directly returned.The customer service backend crashed, and the operations team almost came over to throw our tables over.

To be honest with someone who hasn’t dealt with it before: What exactly is the OpenAI API?

It means you don't need to train large models yourself; you can simply call the APIs of pre-trained models like GPT-4o or GPT-3.5-turbo provided by OpenAI, send a request, and get the returned results. The cost is calculated based on the number of tokens you use. The version that supports up to 128k context per request is now fully available.

The reason we chose it in the first place was very simple: The address formats in European countries are too chaotic. The postal codes in the UK are completely different from those in Germany, and there are also various spelling errors in different languages. The rule base we had written ourselves was updated 8 times in half a year, but it still missed some cases. By using an API for semantic analysis, as long as the correct prompt is provided, the accuracy rate can be directly increased to over 96%.

The 3 real benefits we've obtained; none of them are fictitious.

• Without having to maintain a dedicated algorithm team to tweak the models, the three backends were integrated within a week. In the first month of operation, we saved 32% on the labor costs associated with the three part-time employees.
• Spelling errors and abbreviated addresses that could not be identified by the previous rule library can now be automatically corrected in over 90% of cases. As a result, the rate of order cancellations due to users entering incorrect addresses has decreased by 28% directly.
• Supports mixed input in multiple languages; Polish users can enter addresses in Polish, and Spanish users can enter them in Spanish. There's no need for separate localization adjustments; simply pass the data to the interface, and it will be parsed into a standard logistics format.

Don't just look at the benefits; we almost got into big trouble because of these two mistakes.

The first issue was the default single-model throttling. Previously, we were only using GPT-4o-mini, and the default minute-level throttling was just enough for normal usage. However, on the day of a major promotion, the number of requests increased by three times, which directly triggered the throttling. As a result, 13% of the requests received a 429 error code. To resolve this, we added a degradation logic that directed requests during off-peak hours to GPT-3.5-turbo, which helped to restore service.

The second issue is the misidentification of sensitive addresses. Once, a user entered an address that contained terms related to “military bases,” which was immediately blocked by the API’s content review mechanism. We did not implement a fallback mechanism for such failures, resulting in the order being delayed for two days before it was sent out. As a result, the user left a negative review.

Who should use it? Seriously, don’t waste your money on it.

If you, like us, are engaged in businesses that involve multi-language semantic processing and scenarios where there are an endless number of rules to be created—such as address validation, automatic responses to user inquiries, and generation of multi-language content—and your team does not have a dedicated algorithm development team, using the OpenAI API is much more cost-effective than training your own models.

But if you are working with core data that absolutely cannot be transferred out of the domain, such as financial core data processing, medical privacy data analysis, or in simple scenarios where the volume of requests is very stable and the rules are clear, then don't bother with this. It's cheaper and safer to write your own rules or deploy a small model locally.

3 tips for first-time users, all based on the mistakes we made

• Don't rely on just one model; have at least two models of different quality levels as a backup for fallback scenarios. Use the more accurate model for high-priority requests and the cheaper, less sophisticated model for low-priority requests or during peak usage times. This can save at least 40% on costs and also prevent issues caused by single-model throttling.
• All requests should include retry and fallback logic. Pre-plans for content review interception, rate limiting, and timeout scenarios should be established in advance; don't wait until the interface fails to think of manual handling methods.
• Don't start with the most advanced model right away; first use the cheapest one, GPT-3.5-turbo, to test the performance. If the results aren't satisfactory, then switch to a more advanced model. For most simple scenarios, a smaller model is completely sufficient.

Finally, let's address two common questions that people often ask.

Question: Will there be a high delay when making calls in the European region?
Answer: We are using the Frankfurt node, and most requests are completed within 15 milliseconds. Only a small percentage of requests suddenly take more than 200 milliseconds to complete. Adding a cache can solve this issue without any impact on the business.

Question: Could there be cases where the parsing results are incorrect?
Answer: Yes, we are now forwarding results with a confidence level of less than 80% for manual review. Since we implemented this rule, the error rate has basically become negligible.