AutoFlow

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Inspiration

The idea for AutoFlow came from noticing how difficult workflow reminders and management still is for most people. Powerful tools exist, but they often assume the user already understands triggers, APIs, cron jobs, and data pipelines. We wanted to build something where automation is just downright simple and does not go too overboard. The goal was simple: make workflow automation feel as natural as explaining a task to another human.

What it does

AutoFlow lets users create and run automated workflows by describing what they want in plain language. Instead of manually wiring triggers and actions, users explain the task, and AutoFlow turns that into an executable workflow.

It supports different triggers like scheduled runs, webhooks, and manual execution, and can perform actions such as calling external APIs, sending emails, transforming data, and adding delays. Once created, workflows run reliably in the background, handle retries automatically, and keep a complete history of executions.

How we built it

AutoFlow is built as a full-stack application with a modern, production-ready architecture. The frontend is a React and TypeScript single-page app that provides a clean interface and real-time workflow previews. The backend is powered by Node.js and Express, with PostgreSQL for persistence and Redis plus BullMQ handling background job execution.

At the core is an AI layer that converts natural language instructions into structured workflow definitions. Everything is containerized using Docker and orchestrated with Docker Compose, making the system easy to run locally or deploy.

Challenges we ran into

One of the biggest challenges was translating vague, human instructions into workflows that are both correct and safe to execute. Another major challenge was ensuring workflow ownership and security across all API endpoints.

Handling background execution reliably was pretty tricky, especially retries, failures, and execution tracking, since workflows need to run independently of the user interface. Debugging real-time issues during development, particularly around hot module reloading and queues, required careful iteration.

Accomplishments that we're proud of

We’re especially proud that AutoFlow isn’t just a prototype — it’s a working system with authentication, background execution, error handling, and deployment support. The AI-driven workflow generation works end-to-end, from conversation to execution.

Implementing robust authorization, execution history, and automated testing helped push the project much closer to production quality than a typical hackathon build.

What we learned

This project taught us a lot about designing systems that sit between human intent and machine execution. Small ambiguities in language can lead to big differences in outcomes, so validation and safeguards are crucial.

We also learned the importance of background processing, observability, and security when building automation platforms — things that aren’t always obvious until you try to build one yourself.

What's next for AutoFlow

The next steps for AutoFlow include adding more integrations, improving the AI’s understanding of complex workflows, and introducing better debugging and visualization tools for users. Long-term, the goal is to make AutoFlow powerful enough for advanced users while still being approachable for people with no technical background.