DataFlow

Start with the project checklist: clarify the goal of your application, who will use it, which inputs it should accept, and how you will measure success. Bringing example inputs and expected outputs keeps the Builder conversation focused and reduces the number of iterations you need to reach production quality.

The Builder asks, “What are you building?” and then guides you through clarifying questions before it generates the full application. You describe the use case in natural language, answer follow-ups about users, data sources, and outputs, and Empromptu’s agents assemble the UI, logic, and AI layers—no manual wiring required.

Each build becomes a project that contains every component of your AI application. Inside a project you’ll find tasks—the individual functions such as “Billing Inquiry Handler” or “Review Summarizer.” Dashboards track initial and current accuracy for every task, and the Actions menu gives you direct access to prompt, input, model, and edge-case optimization tools.

Empromptu scores every evaluation on a 0–10 scale, compares initial and current accuracy for each task, and highlights improvement over time. You define success criteria through evaluations, then use automatic optimization, Prompt Families, manual refinements, and edge-case detection to lift scores into the 7–10 range needed for production.

Deploy straight from the Builder using one-click options for Netlify and GitHub, or download the full codebase to host on your own infrastructure. Enterprise teams can keep Empromptu’s optimization loop connected even when they deploy on-premises.