case study - 2025
DropAR
Reimagining Last-Mile Delivery through Augmented Reality
RoleProduct Designer
Timeline6 Weeks
DomainAR · Mobility Tech · Logistics
ToolsFigma, After Effects (AR motion overlays), research interviews + shadowing
Primary Dashboard interface in real time
Background
Last-mile delivery remains the most expensive and error-prone part of logistics accounting for nearly 53% of total shipping cost. Drivers deal daily with confusing addresses, heavy workloads, and constant switching between navigation, calls, and apps.
dropAR was designed to merge real-world navigation and digital workflow into one seamless augmented experience allowing drivers to focus on the road, not the screen.
The Challenge
Delivery agents face three recurring friction points:
Difficulty locating precise drop-off points in dense neighborhoods
Cognitive overload from switching between navigation and delivery apps
Manual input and verification errors causing delays
My Objective
To craft an AR-assisted mobile experience that simplifies last-mile delivery by:
Providing context-aware, real-time guidance
Reducing mental effort and screen dependency
Streamlining scan-to-deliver workflow
53%
shipping cost
35%
higher task load
The Solution
dropAR overlays digital intelligence on the physical world using AR navigation, voice guidance, and smart recognition to simplify each delivery step.
Core features
AR Navigation View: dynamic route arrows projected onto real environments
Smart Parcel Scan: automatic recognition and verification of package IDs
Voice Workflow: spoken updates and commands to reduce touch interaction.
Doorstep Precision Mode: detects entrances and improves micro-location accuracy
Process Methodology
Architectural efficiencythrough intelligent integration
A look into how I streamlined the design lifecycle by embedding AI at key friction points, speeding delivery without losing structural clarity.
How I used AI to move faster
01
Discovery & Synthesis
Used LLMs to cluster interview notes faster, so I could pivot earlier without waiting days to "finish research".
02
Information Architecture
Generated edge-case paths (signal loss, reroutes, mismatch flows) and validated the logic against real delivery steps.
03
Content & Microcopy
Explored tone variants for voice prompts + error states, then rewrote with real delivery context (no generic copy).
04
Prototyping & Iteration
Accelerated layout options + interaction variations, keeping human focus on motion clarity and safety.
05
Quality Assurance
Used structured checklists for states + accessibility: glare readability, contrast, touch targets, offline behavior.
Front end
Truck Camera VisionUnique fingerprints →
Backend
Management and routing softwareParcel & customer infoRecognition algorithmTracking algorithm
Frontend
AR integrated mobile application
LoadingProcessingDelivery
Decision 01
Consolidated Delivery Workflow (No App Switching)
Why
App switching drives cognitive fatigue and slows deliveries.
Options explored
Keep navigation external + delivery inside app · Split AR as a separate mode · Unify navigation + verification + confirmation inside one flow
Final solution
A single delivery loop: route → AR cues → scan/verify → confirm → next stop.
Impact
Lower mental overhead, fewer "where am I / what next" moments, faster progression between stops (qualitative from observation + workflow mapping).
Trade-off
More complexity in state handling (offline, reroute, mismatch), solved through explicit states + quick recovery actions.
Decision 02
AR Cues Over Addresses
Why
Addresses alone fail in dense areas; drivers use landmarks and visual memory.
Options explored
Stronger map UI · Text-based step guidance · AR camera overlay with directional cues
Final solution
Camera view becomes the primary navigation surface, with route arrows and contextual cues.
Impact
Faster orientation at the last 20–50 meters, less second-guessing entrances and drop points (behavioral insight-driven).
Trade-off
Visibility issues in glare/bright light, handled with contrast-first UI and simplified overlays.
Decision 03
Smart Parcel Scan With Verification States
Why
Manual input + verification mistakes cause delays and escalations.
Options explored
Manual entry only · Scan only (no verification) · Scan + match confirmation + mismatch recovery
Final solution
Smart parcel scan with clear outcomes: match / mismatch / rescan / manual fallback.
Impact
Reduced error likelihood during time pressure by making the "correct next action" obvious.
Trade-off
Extra step when scan fails, softened with fast retry + one-tap manual entry.
Decision 04
Voice Workflow + Minimal Touch Completion
Why
Drivers multitask; touch-heavy UIs are unsafe and slow during motion.
Options explored
Full touch UI · Voice-only · Hybrid: voice prompts + simple touch confirmations
Final solution
Spoken updates and commands paired with minimal-touch confirmations and one-hand gestures.
Impact
Less screen dependency, smoother flow when hands are occupied, calmer delivery behavior.
Trade-off
Voice reliability in noisy environments, kept commands limited and always provided touch fallback.
Validation
Quick checks through mock scenarios (bright glare, reflective surfaces, in-motion gestures) to verify legibility and interaction comfort, iterating the overlay density and control placement.
Outcome
dropAR delivers a unified, AR-led delivery workflow that reduces switching, improves verification clarity, and keeps drivers focused through contextual cues and voice support.
I ship clean, scalable UX that respects constraints and delivers measurable outcomes.
0 likes · 0 views · Nov 12, 2026
AR mobile design merging navigation and delivery workflows to reduce errors and cognitive load; features AR navigation, smart parcel scan, and voice workflow.
