People often ask how fast AI-first development actually goes, but for ThinkSpace and SupportSpace that was the wrong question to lead with. We built them to find out whether the method held up once you added the parts a quick demo usually skips, proper authentication, a real deployment pipeline, monitoring, and actual users.
Two proofs of concept
ThinkSpace was a collaborative workspace with canvas boards, task lists, and notebooks, built for teams structuring ideas and decisions together. SupportSpace was a feedback and helpdesk platform, with a widget, a ticket inbox, a knowledge base, and feature voting. Both went from an empty repository to a deployed application, and both used AI coding agents for implementation, with Advancer engineers directing architecture and reviewing the result.
What we kept from day one
- Proper authentication rather than a placeholder login screen
- Error handling and monitoring, so a failure would surface rather than fail silently
- A working deployment pipeline to Azure rather than a one-off manual push
- A clear product vision before either agent wrote a line of code
None of this was bolted on afterwards. The practices that make software supportable are the ones that are hardest to retrofit, so they went in from the first commit.
Since retired
Both products have since been retired. As proofs of concept, their job was to show whether AI-first development could produce something a real user could rely on, and once they had done that, keeping them running added little. They did what we needed them to do.
What carries into Accelerated Build
This is the same discipline we bring to Accelerated Build, Advancer's offer for building bespoke software with AI to production standard. Speed on its own proves little. The test is whether the software that comes out the other end still has the practices that let a business run on it once the build is finished.
