# DataFlow

DataFlow is an enterprise-level analytics prototype for Consumer Packaged Goods (CPG) brands. It showcases marketing performance, AI-driven recommendations, and executive-ready storytelling inside a refined dashboard experience that runs entirely on polished local mock data.

[Open live demo](https://data-flow-app.netlify.app/)

## Everything you need to know about Empromptu

Can’t find the answer you’re looking for? We’ll walk you through this build in real time or hop into your stack via chat.

### What kinds of AI applications can I build with Empromptu?

Empromptu generates complete AI applications that include embedded models, retrieval-augmented generation (RAG), and production infrastructure. Teams build customer support assistants, document intelligence tools, content generators, and review analysis systems that are ready for real users without additional wiring.

### How should I prepare before opening the Builder?

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.

### What happens inside the Builder interface?

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.

### How does Empromptu organize the projects I create?

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.

### How does Empromptu measure accuracy and drive improvements?

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.

### Where can I deploy the applications I build?

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.

## Ready to build analytics experiences that actually ship?

Remix this use case in the Empromptu Builder, plug in your own data, and launch production-ready AI workflows without rewiring your stack.

[Start building now](https://builder.empromptu.ai/)  
[Book a call](https://calendly.com/shanea-leven-pnyq/website)