📰 Key Takeaways

Kuken Industries is a cooling tower manufacturer that has accumulated 70 years of cooling tower engineering drawings. The drawings span a wide range of eras, and every time a piece of equipment had a problem, maintenance staff first had to find the matching drawing in a huge archive of historical files, which was very time-consuming. The company switched to building a drawing search system with the OpenAI API, so engineers can search this seventy-year collection directly. According to the original post, the results are striking: the time an engineer needs to find the drawing required for a repair dropped from almost an hour to about 4.5 minutes. That means finding drawings is no longer the main bottleneck in the maintenance process, and on-site staff can get the right design reference faster and speed up repairs. As for how the system handles the drawings internally, which specific models or features it uses, and the details and scope of the rollout, the original summary doesn’t say. See the source link for more.

💬 JudyAI Lab Take

Kuken Industries has 70 years of cooling tower engineering drawings, and in the past, whenever maintenance staff hit an equipment problem, they first had to dig through a huge archive of historical files. After building a drawing search system with the OpenAI API, the time to find one drawing dropped from nearly an hour to about 4.5 minutes.

What this case shows is that AI often pays off first not by building a brand-new feature, but by handling old data that companies already have and that nobody has had the energy to organize. Seventy years of drawings were always an asset; their value was just being held back by the bottleneck of “can’t find it.” Once finding a drawing goes from almost an hour to about 4.5 minutes, the most time-consuming step in the repair process stops blocking the people on site. For AI builders, the thing to notice is where you choose to start: you don’t have to chase complex automation first. Start with a small action that eats everyone’s time every day, and the impact is easy to quantify, which also makes it easier to convince your team to keep investing. The original post doesn’t explain how the system works internally or how much material was imported, so all we can say for sure is the result, and we shouldn’t speculate about the method.

Try listing the things people in your org most often have to “dig through,” like old files, historical records, or drawings. Measure once how long it currently takes to find a single item, then decide whether it’s worth building a search system for it.

📅 Source Info

🔗 Further Reading