SAN FRANCISCO — Empromptu AI, the company leading enterprises through the transition of static SaaS to self-improving AI-native applications, today announced Alchemy Models, a new capability that allows companies to create, train, and deploy their own production AI models by building the internal and external AI applications they are already creating without any model training expertise.
The launch addresses a growing gap in the current AI tooling ecosystem. Enterprises share their most valuable data with large model providers to compete in an agentic market. Enterprises already employ subject matter expertise required to train models but lack the AI talent to capture it. To prevent disruption from model providers, subject matter expertise is the key to creating an effective data moat. Alchemy closes that gap, adding custom model ownership on top of the application stack.
Companies are spending billions of dollars hiring subject matter experts to document their workflows to sell the training data back to model providers. Companies already have the subject matter experts that they need; what they lack is infrastructure and AI expertise to capture it and use it effectively. Alchemy Models encodes that expertise into an easily usable infrastructure.
“Right now, most companies are renting intelligence,” said Shanea Leven, CEO of Empromptu. “They’re sending their proprietary data into someone else’s model and hoping the economics and policies stay favorable. Alchemy gives them another option. They can build and own the intelligence behind their products. The model providers are Amazon, and the rest of us are knowingly Toys R US in this scenario. Except now, we know exactly what’s going on.”
Empromptu’s platform uses a phased approach to custom model development. Enterprises begin by building AI-driven applications using natural language interfaces, which automatically collect high-quality training data through real-world usage. As workflows generate outputs, subject matter experts label edge cases and validate results, creating the precise training data needed for fine-tuning. This eliminates the traditional barriers to custom model training: no manually curated datasets, no armies of data annotators. Instead, companies leverage their existing workflows to generate training data continuously.
Alchemy simplifies what historically required a full machine learning team. Users define a task in natural language or through the Empromptu builder interface, and the platform handles the rest automatically by:
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Generating synthetic and/or real data using Empromptu’s Golden Data Pipelines
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Selecting and preparing training datasets based on model performance
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Enabling subject matter experts to score and correct outputs from real product workflows
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Evaluating model outputs using automated evaluation frameworks
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Fine-tuning base models for the specific task or application
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Deploying to Empromptu cloud or the customer’s own infrastructure
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Continuously improving the model using agent-driven training loops
The result is a production-ready model that improves over time as it learns from real-world usage. No machine learning expertise is required.
“Right now, a lot of companies say ‘no AI’ because they don’t know how to control it,” said Leven. “The moment they can run AI on their own infrastructure, with their own data and governance policies, the conversation changes completely.”
The launch of Alchemy follows Empromptu’s recent platform expansion, which introduced Golden Pipelines and AI Policies to bring data readiness and governance directly into the AI application development process. Together, these capabilities extend the platform across the full lifecycle of AI systems, from preparing data and building applications to enforcing controls and training models, all within a single environment.
Alchemy is available immediately for enterprise customers using the Empromptu platform. Organizations interested in early access can sign up at empromptu.ai.
