Kuken Industries searches 70 years of drawings in minutes

Kuken Industries makes air conditioning and ventilation equipment for large buildings, shopping centers, and factories. It holds one of the largest shares of Japan’s cooling tower market. These towers release heat from circulating water into the air, helping cool everything from large buildings and malls to equipment in data centers.

When a cooling tower needs a replacement part, Kuken’s engineers turn to an archive of drawings that spans 70 years of installations. For a newer engineer, finding the right drawing often meant asking an experienced colleague where to look and how to read it. Kuken wanted newer engineers to be able to find those records themselves, learn from the decisions they contain, and spend more time figuring out how to make the repair.

Matching a replacement part to the original installation

Parts made today don’t always fit older cooling towers. Without the original drawing to show what was installed, engineers may have to design and manufacture a replacement from scratch.

A search by model or part name can turn up several drawings. Over more than 70 years, Kuken has adapted its equipment to individual customers, so specifications vary by customer and installation date. When the archive was on paper, a search could take half a day. Kuken later digitized the archive, but engineers still had to know which name or number to look up and where to find the file. Newer engineers in particular spent time piecing those details together.

Choosing models that could read Kuken’s drawings

Kuken wanted engineers at every experience level to be able to search the archive on their own. As it explored how AI might help, Kuken connected with Recursive, a Japanese AI startup and an official OpenAI partner.

Searching with a part number or a screenshot

Conversations with field engineers shaped how the search works. Engineers can enter a drawing or part number, or upload an image or PDF of a drawing or parts list.