Empromptu AI has launched Grid Guard, a solution designed to prevent AI data centres from damaging their power infrastructure through sudden GPU-driven demand spikes. When thousands of GPUs execute operations simultaneously, power demand can swing by tens of megawatts in milliseconds, potentially damaging generators and requiring costly infrastructure expansion. Grid Guard addresses this by staggering workloads by 50–200 milliseconds, transforming sharp power spikes into gradual ramps. Early deployments have shown an average 80% reduction in power volatility without meaningful impact on AI workload performance. The system predicts power swings, smooths them through workload orchestration, controls infrastructure response, and provides economic signals to encourage efficient operations. Grid Guard is already engaged with a major power company ahead of live deployment.

Empromptu launches Alchemy Models: the Next wave of AI after vibe coding. A new platform capability allows companies to build, own, and continuously improve their own AI models instead of renting intelligence from external APIs. 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: * Generating synthetic and/or real data using Empromptu's Golden Data Pipelines * Selecting and preparing training datasets based on model performance * Enabling subject matter experts to score and correct outputs from real product workflows * Evaluating model outputs using automated evaluation frameworks * Fine-tuning base models for the specific task or application * Deploying to Empromptu cloud or the customer's own infrastructure * 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. May 15, 2026 Prev Next 1 of 42,933 As AI adoption expands, companies are encountering three major challenges. Many AI coding tools generate working code but lack the infrastructure required to run AI in production - without structured data pipelines, evaluation frameworks, and governance controls, applications that work in demos often fail when real users and messy data enter the system. Most AI applications today also rely entirely on external model providers, creating concerns around data control, vendor lock-in, and long-term cost exposure. And API-based models can become expensive as usage increases: fine-tuned models optimized for specific tasks can often deliver higher accuracy at significantly lower cost. Early enterprise adopters are already seeing measurable results. In internal benchmarks, custom models built using Alchemy have reduced inference costs by 40-80% and increased their accuracy rates 25-30%. Ascent Health increased their accuracy rates on their learning application by 30% in the first run. Organizations across sectors such as financial services, healthcare, legal technology, and retail are using Alchemy to build models tailored to their industries, training them on proprietary datasets for risk analysis, compliance monitoring, diagnostics, contract review, and demand forecasting. Many enterprises remain cautious about adopting AI because they lack governance processes or worry about exposing proprietary data to external providers. Empromptu was designed to address those concerns directly. The platform includes governance policies, audit logs, environment controls, evaluation pipelines, model drift monitoring, and rollback paths, enabling companies to deploy AI systems safely inside regulated environments. "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. [To share your insights with Aithority, please write to [email protected]]

Empromptu AI has launched Alchemy Models, a platform enabling companies to build, train and deploy their own AI models without machine learning expertise. The capability allows enterprises to create custom models using data from their existing AI applications, rather than relying on external API providers. The platform addresses a gap where companies lack AI talent to capture their internal subject matter expertise. Alchemy collects training data automatically through real-world application usage, with experts labelling edge cases and validating results. This eliminates traditional barriers like manual dataset curation and data annotation teams. "Right now, most companies are renting intelligence," said CEO Shanea Leven. "Alchemy gives them another option. They can build and own the intelligence behind their products." The announcement comes as enterprises increasingly seek to create data moats and reduce dependence on large model providers.