Empromptu AI launched Alchemy Models, a platform that automatically captures training data from enterprise AI applications already in production. The system routes validated outputs from subject matter experts directly into fine-tuning pipelines, continuously improving models without requiring a dedicated machine learning team.

Most organizations waste the signal embedded in their production workflows. Every query processed and every correction made by an expert represents potential training data. Enterprises currently lose this information entirely.

Alchemy Models intercepts this data automatically. When a subject matter expert validates or corrects an AI output, the platform captures that interaction and feeds it back into the model's fine-tuning process. The resulting improved model weights remain the enterprise's property.

The approach addresses a real bottleneck. Building custom AI models typically demands scarce ML engineering talent and expensive data labeling infrastructure. Empromptu's platform eliminates that friction by converting existing workflows into continuous learning systems.

The mechanics are simple. Production applications generate queries and outputs. Subject matter experts review and validate results as part of normal operations. Alchemy captures these validated outputs, batches them, and uses them to fine-tune the deployed model. This creates a feedback loop where business operations directly improve model performance over time.

Ownership matters here. Enterprises retain full control of the resulting model weights rather than licensing a generic model from a third party. This approach lets companies build proprietary models tailored to their specific domains and processes without hiring ML teams.

The timing fits the enterprise AI moment. Companies have deployed countless AI applications but struggle to improve them systematically. Most lack the infrastructure or expertise to conduct ongoing model refinement. Alchemy Models converts that gap into an advantage by automating the capture and application of real-world training signal.

The platform targets enterprises already committed to AI but frustrated by static model performance. Rather than replace existing applications, Alchemy works within them, turning production workflows into self-