Experience design

Experience design is a broader discipline than UX/UI. It covers the full arc of how a person interacts with your product or service — from first awareness through to repeated use, including research, service design, interaction design, and visual design. UX/UI is one layer of experience design, not the whole picture. We work across all of it, so what gets designed is coherent end-to-end rather than optimized screen by screen.

AI accelerates the parts of the design process that traditionally consumed the most time, research synthesis, pattern recognition, rapid prototyping, and accessibility evaluation. This means we reach validated design decisions faster, with more evidence behind them. The creative and strategic judgment still comes from our designers; AI handles the volume work that used to slow them down.

Our design practice is built to integrate directly with Gen-e2™ engineering from day one. Research findings, user stories, and design specifications flow into a shared repository rather than being handed over in a document at the end of a design phase. This eliminates the gap between what was designed and what gets built, one of the most common sources of quality loss in digital product development.

Both. For new products, we run discovery and design from scratch, establishing the research foundation and design system that the build phase inherits. For existing products, we conduct design audits, identify friction points, and redesign specific flows or surfaces without requiring a full rebuild. The scope is shaped by where the most user and business value can be unlocked.

Accessibility and inclusion are built into our process rather than checked at the end. We design to WCAG standards, test with diverse user groups, and use AI-assisted tooling to catch accessibility issues early in the design cycle. This keeps remediation costs low and ensures your product works for the widest possible audience from launch.

Most engagements begin with a focused discovery phase: user research, stakeholder interviews, and a review of existing product analytics. Working in our AI-assisted product workspace, research agents help gather and synthesise this input, so we reach a clear picture of user needs and business goals faster than traditional discovery. From there we move into design, prototyping, and validation, with everything flowing into the Gen-e2™ build cycle wherever it applies. Scope and timeline vary by product complexity, and we agree both before committing.