GOPOINT / BUILD NOTES
This site runs on Replit.
We built this site as a practical example of an AI-first website workflow: work in the codebase, review the changes, and publish through a controlled deployment.
THE APPROACH
A working product, not a static pitch.
The site brings together a studio homepage, seven capability pages, a contact journey, an AI audit, and a separate YouTube Intelligence product. It uses a shared design system while keeping the studio and data product visually distinct.
WHAT IT USES
Built with tools the team can maintain.
The current site is a React and Vite frontend served with Express, hosted on Replit.
- 01
Pages, forms, attribution, and metadata are part of the same application.
- 02
Content and design tokens are organized for ongoing updates.
- 03
Public YouTube data remains separate from private channel-level records.
WHY SHOW THE WORKFLOW
Make the operating choices visible.
We built this site to use the same things we discuss with clients: pages with a clear purpose, content that can be maintained, a working inquiry path and separate product experiences where the audience needs something different. The site is not a promise that every company should use the same platform or tools.
An AI-first workflow can be useful when a small team needs a direct path from describing a change to reviewing it and publishing it. It still needs a real person to check the content, understand the consequences and decide when it is ready. We keep that review step visible because speed without ownership is not a good publishing system.
Our implementation changes over time. When a tool, page or process changes, the public site should be updated to match rather than describing an old architecture as if it were permanent. The important lesson is the operating practice: make decisions clear, keep changes reviewable and leave the team able to maintain what ships.
The homepage and intake experience remain focused on helping a visitor state a need, see where we might start and understand published pricing. Those decisions matter as much as a technical stack. They connect the public description of our work with a real next action, and give the team context to respond to an inquiry.