TIM (Israel) — On-Demand Jobs App
TIM is a right-to-left, on-demand jobs marketplace that lets people hire vetted "Timos" for everyday tasks — dog-walking, handyman work, deliveries, even late-night grocery runs.
Mobile Design
User Research
Information Architecture
UI-Kit
Presentation
User Persona
UX/UI Design
Figma
XMind
01 — Overview
Project Overview
TIM is a right-to-left, on-demand jobs marketplace that lets people hire vetted "Timos" for everyday tasks — dog-walking, handyman work, deliveries, even late-night grocery runs. Our three-person squad (PM + 2 UX/UI) had just two months to transform the founders' "time = money" vision into a launch-ready MVP, tackling trust, instant booking, and clear pay as core pain points. Through 25+ rapid concept iterations, user research, IA planning, and usability tests, I led the end-to-end UX/UI effort — shaping a validated product concept, scalable design system, and developer-ready guidelines that set the stage for a higher job-fill rate and a 4.7-star App Store debut.
02 — Problem
Problem Statement
Urban Israelis struggle to reclaim personal time because current gig apps are slow, unclear on pay, and feel risky, especially when a stranger must enter the home. Our challenge was to create a service that lets them turn time into money through instant, trustworthy bookings.
03 — Challenges
Key Challenges
1
Mind-set shift — selling "time = money"
Early concepts fell flat; 25 iterations before the right hook resonated.
2
Trust & safety
Users will only hire people they'd let into their homes or walk their pets.
3
Task diversity
Eight very different job categories (Pets, Indoor, Outdoor, etc.) had to feel coherent yet easy to filter.
4
RTL localisation
Hebrew layout and microcopy had to read naturally while preserving global patterns.
5
Compressed timeline
A two-month window demanded lean, evidence-driven decisions.
04 — Process
Design Process
I used the Design Thinking approach to solve the challenges we faced.
05 — Discover
Discover Phase
- Conducted 10 seeker + 6 provider interviews
- Competitor teardown (TaskRabbit, local apps) to map gaps in booking speed & pay clarity
06 — Define
Define Phase
- Synthesised insights into a problem statement and three opportunity areas (instant match, transparent pay, trust signals)
- Prioritised success metrics: posting time, task success rate, user trust score.
- We created an information architecture (IA) for the key user flows we had selected.
07 — Ideation
Ideation Phase
Ran crazy-8s and decision matrix workshops → 25 concepts → narrowed to 1 "perfect" concept after iterative user voting.
25
Concepts generated in ideation
15
Iterations in research loop 1
10
Iterations in research loop 2
2
Research loops with real users
1
Final "perfect" concept shipped
08 — Prototype & Testing
Prototype & Testing
We built high-fidelity Figma prototypes and tested them with clients and potential users. Each prototype featured different interactions, allowing us to gather insights that informed better design decisions and feature prioritization.
09 — Design
Design Phase
Crafted an RTL-ready lite design system (8-pt grid, tokens, light/dark themes). Delivered all assets, notes, and annotations to the client's development team. Designed high-fidelity screens with micro-interactions for booking, payment breakdown, and trust badges.
10 — Results
Final Result — A Polished, Ready-to-Ship Product
95%
Task-success rate across all key flows in usability testing.
SUS = 85/100 · n = 15 (Maze + guerrilla)
−70%
Average "create-order" flow cut — from ~3 min down to <1 min.
Measured via Maze internal testing
4.7★
App Store debut rating, driven by intuitive onboarding and trust-first design.
Launch day · Israeli App Store
What was delivered
✓Created a design system — delivered all assets, notes, and annotations to the client's development team. Designed high-fidelity screens with micro-interactions for booking, payment breakdown, and trust badges.
✓110+ hi-fi, RTL-ready mobile screens — token-based mini design-system (8-pt grid, light/dark, JSON hand-off) · Annotated IA & user-flow specs.
✓25 design concepts explored → 1 "perfect" concept chosen after two research loops (15 + 10 iterations).
✓95% task-success rate · SUS = 85/100 · Average "create-order" flow cut ↓ 70% (≈3 min → <1 min) — Internal Maze & guerrilla tests, n = 15.
✓Mini design systems accelerate even MVPs — tokens + RTL components cut dev time and future-proof scaling.
✓Quantify everything early — lab metrics (SUS, task time) gave stakeholders objective proof, smoothing sign-off and funding pitches.
✓Founders successfully used the prototype in investor demos.
