How AI changes the process
Every stage of a web project has been reshaped by AI. Here's what that looks like in practice — the way things used to work, and what we do now.
Weeks of workshops, stakeholder interviews and documents passed between teams. Briefs were taken at face value. Assumptions went unchallenged until development was already underway.
AI analyses the brief against patterns from hundreds of similar projects — surfacing gaps, challenging assumptions and generating alternative approaches before the first meeting ends. Scoping that took two weeks now happens in days.
A lead developer manually mapped out content models, page structures and component libraries. Prototyping meant building from scratch, with no way to test structural decisions without significant dev time.
AI generates draft content models, schema structures and component scaffolding based on the brief. Our senior developers review and refine — spending their time on architecture decisions, not boilerplate setup. First prototypes appear in days, not weeks.
Every component hand-coded from the ground up. Code reviews were bottlenecked by availability of senior developers. Bugs were caught late, often in staging or even production.
AI accelerates code generation for standard patterns — forms, layouts, integrations, data fetching — while senior developers focus on the complex logic that actually requires human judgment. Automated AI code review catches issues in real time, before they ever reach a pull request.
Content was a separate workstream, often outsourced. Copy arrived late, didn't fit the design, and required rounds of back-and-forth between copywriters, designers and developers. Launch dates slipped.
AI generates draft content that respects your brand guidelines, content model and page structure — directly inside the CMS. Editors refine rather than start from scratch. Content and development run in parallel, not in sequence.
Manual testing across browsers and devices. Test scripts written by hand. Edge cases discovered by accident. QA was the phase that always got compressed when timelines slipped.
AI generates test coverage based on the actual codebase, identifies edge cases from content structures, and flags accessibility issues automatically. Testing starts earlier and covers more ground — without adding time to the schedule.
After launch, changes required new development cycles. Even small content updates needed developer involvement. Optimisation meant waiting for enough traffic data, then starting a new project.
AI-powered content tools let your team iterate independently. Performance insights surface faster. When you need development changes, our AI-assisted workflow means updates that used to take a sprint now ship in days.
