I built the first version in around two-and-a-half hours. Most of the initial work was figuring out what data I needed, where to pull it from, and how to connect it. I actually started by writing out the workflow on paper, taking a picture of my notes, and uploading it into Pave. Even with rough handwriting, it was able to understand the structure I wanted and generate the starting point.

From there, I kept iterating in natural language. If something was missing, I would ask questions like why the journal entries were not showing correctly or ask it to adjust the dashboard logic. I also experimented with small workflow automations, like email notifications whenever a developer updated a feature. I also thought about permissions early on, since not everyone needs access to the same information.

More visibility, less manual work

The biggest improvement was visibility into feature status, employee hours, and capitalization logic all in one workflow, instead of pulling information from different systems and trying to piece it together. That makes the process easier to follow and explain at audit.

The email notifications also reduce manual effort, and role-based permissions make sure team members would only see the information relevant to them.

One thing that stood out to me was how quickly I could get a working version built once I understood the workflow clearly. I did not spend a lot of time planning upfront. I focused on the real process first -what data we use, how capitalization decisions are made, and what auditors usually ask for — then built around that.

Even a simple prototype was enough to show how much easier the process could become if everything lived in the same place. I plan to keep building on this, especially around permissions and making the workflow more complete.

Five things I learned building with Pave

• Start with what you need to track. I focused on understanding where a feature stands and what data is needed for capitalization. Once that was clear, the rest followed.
• Pull data from the right places early. I worked with data from P&D Engineering Central and Paylocity from the beginning, so the app reflected how the process actually works.
• Keep the structure simple. I did not try to build everything at once. I focused on getting the basic tracking in place first.
• Think about permissions upfront. Different teams should not see the same information. It's easier to plan that early than fix it later.
• Add small things that reduce manual work. Even something like email notifications helps, because it reduces the need to keep checking for updates.

Try this prompt

Want to build something like this? Try Pave and enter this prompt:

Design a secure, user-friendly internal-use software capitalization app aligned with ASC 350-40. The app should support collaboration between Accounting, Engineering, and HR. The purpose of the app is to track feature development labor, determine capitalization eligibility, classify labor as capitalizable or expensed, generate journal entries, create amortization schedules after features are placed in service.

About Pave:

Pave is Quickbase's AI app builder for teams that need to turn ideas into real, usable business apps fast. Unlike prototype-only tools, Pave helps teams create production-ready apps with data, governance, permissions, hosting, and deployment built in. Built on Quickbase's secure infrastructure, Pave gives businesses a more practical, controlled path from experimentation to execution. Start building now at quickbase.com/pave.