Large research organizations carry an old waste: team B unknowingly repeats the sample analysis team A finished, and team C retries a material combination that failed years ago. We built a system that attacks this directly at a major Korean corporate research lab.
What was the problem?
Sample and material analysis results were scattered across team storage and personal laptops. Finding anything meant chasing down the owner, and when that person left, the research left with them. Duplicate work burns budget and time at once.
What did we build?
We built an internal knowledge base keyed by material and atomic model, where every team’s research history accumulates in one place. Which material was analyzed under which conditions, with what outcome — all of it lands on a single material card. On top of it, we layered an AI system that turns sample and material analysis data into production decisions.
Where does the AI come in?
- Recommendation — registering a new experiment surfaces similar materials and prior research first. You learn "this was done three years ago" before running it again.
- Classification — sample and material data is classified automatically, removing the cost of curating the knowledge base by hand.
- Modeling — materials are modeled on atomic structures, narrowing candidates before anyone books lab time.
Did duplication actually drop?
Finding material data dropped from hours to seconds, and whether a planned study was already completed is now checked at registration. The structure changed, not just the tooling: knowledge became an organizational asset instead of personal memory.
The most expensive experiment in a lab is the one you repeat because nobody knew it was finished.