The observation that started it

It started at Bengaluru City Railway Station. A young couple, four suitcases, arguing with an auto driver who wanted ₹600 to take them 2 km because they had nowhere to drop their things. Their flight was the next morning. A hotel meant paying for a full night. The station cloak room was closed.

I sat with that observation for a year before I designed anything.

Secondary research

Thirty minutes of structured research with Claude as a research partner. Two findings did most of the work.

📌 Artifact placeholder — market-landscape diagram (global solutions vs Indian gap) will sit here.

A vague problem produces a vague product. To move beyond observation, structured interviews with the user segments most affected:

• 5–7 participants across three segments: business travellers, students/families, tourists.
• Semi-structured 30-min sessions, in-person at Bengaluru City Railway Station and Kempegowda International Airport.
• Focus areas: current workarounds, payment willingness, trust signals required.
• Synthesis: affinity mapping, empathy maps, common pain themes.

This section will update with real findings as interviews complete.

Initial empathy synthesis

Based on observation and secondary research — to be refined with interview data.

Says

* This image is AI generated

“Where am I supposed to put these?”
“Is it safe to leave them here?”
“Will I make it back in time?”

Thinks

* This image is AI generated

“This is going to ruin my morning.”
“I should have shipped them ahead.”
“Why does no app exist for this?”

Does

* This image is AI generated

Pays for a hotel night they won't use
Drags luggage through cafés and meetings
Hands a bag to a stranger for ₹100

Feels

* This image is AI generated

Anxious · Frustrated · Stuck · Embarrassed · Resigned to risk

Concepts considered

Four directions, evaluated against viability, desirability, and feasibility for a 3-hour build.

Divergent thinking — notebook + prompts

No Figma in this project. Exploration happened in two media:

• Notebook sketches — pencil sketches of screen layouts, main-screen variations, and flow diagrams. The only pre-prompt artifact.
• Prompt brainstorming with Claude — written exploration of alternative IAs, “what if we open on a list?”, “what if KYC is at the end?” Claude as a divergent-thinking sparring partner.

📌 Artifact placeholder — 2-3 notebook page photographs will sit here with one-line captions.

Information architecture

GoBags app
├── Onboarding (4 screens)
│   ├── Welcome
│   ├── Aadhaar consent
│   ├── Permissions
│   └── Map handoff
├── Discovery
│   ├── Map (default)
│   ├── List view
│   └── Filters
├── Storage detail
│   ├── Photos
│   ├── Hours · Capacity · Rating
│   └── Reserve
├── Booking
│   ├── Duration & price
│   ├── Payment (UPI default)
│   └── Confirmation + QR
└── Add-on
    ├── Delivery scheduling
    └── Delivery tracking

📌 Artifact placeholder — clean IA diagram will replace this code-style tree.

Two named user flows

Usability test protocol

• Method: Moderated remote usability tests via Maze and Figma prototype.
• Participants: 5 users matching the Anaya persona. Recruited from LinkedIn travel groups and college-alumni networks.
• Tasks:
  1. “You've arrived in Bengaluru with two suitcases. Find storage near MG Road.”
  2. “Complete a booking and pay.”
  3. “Schedule delivery to a hotel later today.”
• Success metrics: Task completion rate, time-to-complete, error rate, SUS score, post-test interview themes.

What I'm specifically testing for

Early iteration hypotheses

Three things I already suspect will need work, even before testing:

• Aadhaar screen will need a one-line trust statement (“Your data is encrypted and never stored”).
• Partner photos matter more than ratings. People judge trust visually first.
• Booking confirmation probably needs an offline-accessible QR fallback for users on patchy mobile data.