Building a real-world AI automation app from scratch involves a lot of moving parts. Rather than just sharing a finished product, we wanted to pull back the curtain and show the actual behind-the-scenes engineering.
In this video, I walk through the complete working processes at our office, Astapor Technologies. We break down how our team takes an idea, structures the logic, and builds out a functional, high-performance app.
{https://youtu.be/bJfEyOutOl8?si=Mogjgx4H4ObqTuGJ}
Our Development Workflow
We break our building process down into three main phases to keep the project clean and manageable:
1. Mapping the Automation Logic
Before writing a single line of frontend code, we map out how data moves between systems. For AI applications, ensuring the prompts, API calls, and webhooks connect flawlessly is the most critical step. If the underlying automation logic is messy, the app will be slow.
2. UI Layout and Mobile Development
Once the backend workflows are solid, we move on to building a clean, responsive user interface. We focus on creating a layout that makes complex, automated tasks look incredibly simple for the end user.
3. Testing and Optimization
An automation tool is only good if it is reliable. We spend a lot of time running the application through various mobile simulators and device testing to catch edge-case bugs, optimize response times, and ensure smooth data handling.
What is your team's workflow?
Whether you are building internal office tools or client-facing applications, structuring your automation early saves hundreds of hours of debugging later.
Watch the full video above to see our screen share and a closer look at our setup.
I would love to hear from other developers: how does your office handle planning for complex AI builds? Do you have a favorite tool for mapping out your logic? Let me know in the comments below!
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