Marketplace
Comparison
Engine
An automotive marketplace comparison engine, designed to stand apart from the competition while surfacing the USPs only this platform can claim. One engine, two surfaces: SEO-indexable fixed pages and a freeform tool.

Overview
I led design of a comparison engine serving two surfaces at once: SEO-indexable fixed pages and a freeform tool. I designed it by building it: a fully functional prototype, made with AI-assisted development, that was user tested before a line of production code was written.
Client
Carwow
Role
Product Designer + Design Engineer
Timeline
2026
Platform
Web · Responsive
Team
1 designer-builder · cross-functional squad
The problem underneath the problem
Every comparison tool has the same trap hiding in it. People search and think at model level: this car versus that car. But every honest figure on screen belongs to one specific derivative, one real configuration with its own price, engine and boot. Show model-level numbers and you are lying politely. Force a trim picker before showing anything and you have lost the person and the search ranking. I led discovery across automotive competitors and the best analogues outside it, from consumer electronics spec tables to price comparison journeys, then mapped five buyer personas by journey position rather than demographics. The architecture fell out of the research: model carries the identity, a pre-selected sensible derivative carries the honest figures, and the user can drill down when they care.
The second decision was the one I defended hardest. The fixed, crawlable pages and the freeform pick-your-own tool had to render through a single engine with no structural distinction. Not two products that look similar. One engine, two doors in. That call shaped everything downstream: canonical state, inline add and swap, per-column derivative controls, difference highlighting, win and lose rules for the full spec grid, and graceful degradation when a derivative goes out of production. I ran a cross-functional hive-mind to pressure-test the problem space with research, SEO, engineering and design in one room, with a clear ambition: input from many, output that feels designed by one person.
Product design + prompt layer
2026
A vision you can drag
I do not pitch visions with slide decks, and I did not design this one in flat mockups either. I built it. Using AI-assisted development I made a fully functional prototype: real cars, real derivatives, real figures, a slider hero that lets you physically drag between the two cars so comparison feels like weighing something rather than reading a table. Building it was the design method. You cannot feel whether a derivative switch is honest or jarring in a static frame; you can in ten seconds with a working one. Underneath it sat a prompt layer designed like any other system component: AI-generated verdict lines with voice rules, length caps, retry and fallback behaviour, and caching, written up so the build inherits the rules.
The prototype did the two jobs a deck never can. It was the experience design: every interaction decision was made by using the thing, not imagining it. And it de-risked the investment: usability testing ran on the functioning prototype with real users, and stakeholders drove it themselves rather than watching a walkthrough, all before committing an engineering team to the full build. What we learned was folded back in, then the vision was reconciled against version one; every ambitious idea annotated into the near-term spec, parked with a reason, or cut. The shipped direction kept what mattered: one engine, two surfaces, model identity grounded in an honest derivative, and a verdict that gives people permission to decide. Discovery, research, architecture, prompt design, working software, evidence. One person, end to end.
In numbers
450+
Models covered
9,000+
Real derivatives covered
40M+
Possible head-to-heads