P / 01
Case Study · Featured
2024 — 2025 · Enterprise AI
AI Training Studio
A file-management workflow redesign for training enterprise AI agents — moving trainers from a spreadsheet-like triage into a dedicated studio where labeling, versioning, and agent evaluation share one spatial model.
Problem
Trainers bounced between three tools to label, version, and test agent behavior. Each handoff dropped context; the team shipped slowly and blamed the model. The decision cost — not the compute cost — was the bottleneck.
Approach
Collapsed three tools into one studio with a single state model — every file, label, and agent run hangs off the same timeline. Removed one decision per session (the tool-switch) instead of adding another feature. Shipped the first useful slice in three weeks; iterated with real trainers.
Outcome
+123%
Training efficiency
3×
Fewer tool-switches
