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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. Cut the training flow from 6 steps to 4 by merging redundant uploads and replacing tutorial-driven onboarding with guided prompts. Shipped the first useful slice in three weeks; iterated with real trainers.
Outcome
6 → 4
Steps to ship a trained agent
+123%
Feature adoption
