Kuken Industries uses OpenAI API to search 70 years of drawings
OpenAI has published a customer story about Kuken Industries, a company that uses the OpenAI API to search its archive of 70 years of cooling tower drawings.
The headline result is a time saving on repair work. According to OpenAI, engineers can now find the drawing they need for a repair in 4.5 minutes. Before, the same lookup took nearly an hour.
That is the whole of the account: one company, one archive spanning seven decades of cooling tower drawings, and one before-and-after figure for locating the right drawing. The text gives no detail on which OpenAI model or endpoint is involved, or on how the drawings are prepared and searched.
Key facts
- Kuken Industries uses the OpenAI API to search 70 years of cooling tower drawings.
- Engineers can find the drawing for a repair in 4.5 minutes.
- The same lookup previously took nearly an hour.
- The claim comes from OpenAI's own customer story, in the publisher's voice.
Why it matters
Old engineering drawings are a practical test for AI search: decades of material, and a repair that waits on finding the right sheet. The reported drop from nearly an hour to 4.5 minutes per lookup is the kind of concrete, everyday gain that customer stories like this one are meant to show. It is a single case, not a trend.
Who it affects
The story concerns engineers at Kuken Industries who look up cooling tower drawings for repairs. By implication it is relevant to other firms holding long-lived technical archives, though the source itself speaks only about Kuken.
How to use it
The source gives no implementation steps. What it states is the pattern: point the OpenAI API at an archive of drawings so engineers can retrieve the one they need for a repair. Which model, which endpoint, and how the drawings are digitised, indexed or searched is not stated, and neither are cost or deployment timescale.
How solid is it
This is a first-party OpenAI customer story, and the figures are OpenAI's own account. Both the 4.5 minutes and the 'nearly an hour' are given without saying whether they are means, medians or typical values, or how they were measured. No quotes from Kuken staff or OpenAI are given, and no publication date is stated.
Risks and caveats
Treat the numbers as a single company's reported result, not a benchmark. Accuracy figures, the number of engineers involved, and cost are not given, so the saving cannot be weighed against effort or error rates. The 'nearly an hour' baseline is approximate by its own wording.