
- Client
- Own product · FANG
- Year
- 2026
- Timeline
- 9 weeks to launch · live 7 months
- Role
- Product strategy, Brand & UI, Full-stack build
- Stack
- Next.js · Postgres · Claude API · Stripe · Vercel

Designers don't know why they're not getting hired.
Portfolios get rejected silently. Dazzle's founders wanted a product that reads a designer's work the way a hiring manager does, and tells them, in one number and a short list, what to fix first. The hard part was making an AI judgement feel fair, specific and worth paying for.
- ProblemNo feedback loop between applying and rejection
- BetA readiness score designers trust more than a friend's opinion
- ConstraintLaunch before the autumn hiring window
What we built.
Dazzle in four parts, the pieces that carried the product, in the order we shipped them.
- (01)
Readiness score
One number from 0–100 with a confidence ring, built from weighted benchmarks across portfolio, case studies, resume and LinkedIn.
- (02)
Benchmark engine
Structured rubric per asset, run through Claude with retrieval over our own dataset of hired-vs-rejected portfolios.
- (03)
Fix-first plan
Each weak benchmark expands into a prioritised checklist and before/after examples, re-scored on every upload.
- (04)
Brand & site
Warm, editorial identity that sits apart from the dark dashboards designers already ignore.


What it did.
Numbers shared as ranges; specifics on request.
- (01)
Shipped in nine weeks
Scoped to what proves the business: upload, score, plan, pay.
Scope → launch - (02)
500+ users in seven months
500+ designers scored in seven months, on web and iOS.
Traction - (03)
Handed to a team
Typed codebase, documented rubric, admin tools, no studio dependency after launch.
Handover
Build something that ships.
Start a project