Product Design / 2026 · Hamburg

Smart Zoo
Companion

From navigation to contextual confidence

Visitors don't lack information. They lack context.
A companion that notices when a moment might be worth their attention — then gets out of the way.

Role
Product Designer · UX Lead
Duration
16 weeks · 2026
Methods
Field Research · Service Design · IA · Prototyping
Problem Framing
01 / Problem Framing

The project started as a
navigation challenge.
Research revealed a deeper problem.

What was asked
"Help visitors find things"
Route optimization between enclosures
Real-time location mapping
Shortest path to popular exhibits
"We just went where the map said. Missed the feeding anyway."
What was actually needed
"Help visitors feel confident enough to choose their own path"
Context before they face a decision
Confidence without reducing discovery
Spatial awareness, not optimized routes
"I just wanted to know if it was worth the walk right now."

This distinction changed everything downstream.

02 / Research & Discovery
7 field sessions
23 visitor interviews
4 zoo environments
Zoo staff & keeper perspectives
Contextual Inquiry Observational Study Expert Interviews Journey Mapping
F.01
Visitors navigate by confidence, not by map
All observed visitors who paused, turned around, or appeared frustrated were not lost — they were uncertain. They knew where things were. They didn't know if the decision they were about to make was worth it.
Field note"Should we stay here a little longer, or is something better happening nearby?" — parent, session 03
F.02
Information asymmetry creates decision paralysis
Visitors know surprisingly little about current conditions — animal activity status, crowd density, feeding times — despite this being the primary input to their decisions. Staff know everything. The gap creates friction at every junction.
Field note"We don't want to miss the feeding again." — family group, session 06, at the third enclosure junction of the day
F.03
Serendipity is deeply valued — and fragile
When asked about their best zoo memories, 87% of interviewees described an unexpected encounter — not a planned highlight. Any solution that over-optimizes the path destroys the very thing visitors came for.
Field note"That moment made the whole visit worth it. We never would have found it with a checklist." — couple, session 07
Field observation · Hamburg Zoo · Session 04
"The problem isn't that visitors can't find the penguin enclosure. The problem is that they don't know if it's worth the 12-minute walk right now — and there's no way to find out without walking there."
Key Insight
03 — Key Insight

Visitors don't lack
information.
They lack context.

A companion that interprets the environment
and helps people notice what matters right now.

Confidence before optimization
Context before recommendation
Discovery over checklists
More value in the zoo than in the app
checklist model
territory model
The Moment Finder
03.5 / The Moment Finder

Feel the product logic.
Don't just read about it.

Each visit contains three moments where a small piece of context can change everything. Hover or click a signal point to explore each one.

ZOO TERRITORY ACTIVE ZONE CONFIDENCE FIELD SYSTEM M.01 Arrival M.02 Decision M.03 Late visit W E N S
Hover a signal point to explore a moment
Moment 01 · Arrival · Orientation
Visitor
"Should we stay here a little longer, or is something better happening nearby?"
System reads
Crowd density is low. Animal activity at the savanna section is peaking right now. Your group has 2h 40min remaining. The savanna is 8 minutes from here.
Activity increasing · Good window now
→ outcome
Family decides to go. Not because they were told to — because they finally had enough context to choose with confidence.
Moment 02 · Decision · Crossroads
Visitor
"What is happening nearby? We don't want to miss the feeding again."
System reads
Penguin feeding begins in 18 minutes. You are 12 minutes away. The reptile house — still on your list — is on the way. Both are reachable before your planned exit time.
Feeding at 14:30 · Time is sufficient
→ outcome
They arrive two minutes before the feeding. That moment becomes the memory they carry home.
Moment 03 · Late Visit · Fatigue Point
Visitor
"The kids are getting tired. Are we missing something still worth seeing?"
System reads
One item remains on your list. It is 6 minutes away, currently quiet, and low-energy — suitable for a winding-down visit. After that, the exit is 4 minutes away.
Low energy required · On the way out
→ outcome
They go. The kids are calm. Nobody feels like they missed anything. The visit ends well.
"Should we stay or move?"
"What is happening nearby?"
"Are we missing something?"
Interact · Select a moment
Journey of Uncertainty
04 / Journey of Uncertainty

A zoo visit isn't a linear experience. It's a series of small decisions made under conditions of incomplete information — each one shaped by uncertainty about what's worth it right now.

ARRIVAL ORIENTATION EXPLORATION DECISION RESOLUTION UNCERTAINTY LEVEL "Which way first?" "Is this worth it right now?" "The kids are tired — what's still worth seeing?" MOMENT 01 MOMENT 02 MOMENT 03
Moment 01 · Arrival
The first fork in the path
"Which direction gives us the best start? We have about three hours."
System reads: crowd density + animal activity + group profile
Moment 02 · Decision
Is this worth the walk?
"We don't want to miss the feeding again. Is something better happening nearby?"
System reads: time remaining + current conditions + what's on the way
Moment 03 · Fatigue point
The late-visit edit
"The kids are getting tired. What's still worth seeing before we leave?"
System reads: energy state + remaining priorities + exit proximity

Three moments define the entire experience.
The system is designed for exactly those three.

05 / Contextual Confidence Engine

The system doesn't recommend. It reads the environment and builds a picture of what's worth noticing — surfacing context at decision moments, then going quiet again.

ENVIRONMENTAL CONTEXT · Crowd density signals · Animal activity status · Weather & visibility · Feeding session times VISITOR STATE · Group composition · Time remaining · Priorities expressed · Energy level CONFIDENCE OUTPUT Context cards at decision moments Quiet during active exploration Never prescriptive — always contextual
Environmental Context
Real-time crowd density per enclosure
Animal activity status (keeper-updated)
Next feeding sessions & special events
Weather impact on outdoor areas
Visitor State
Group type: family, couple, solo visitor
Time remaining in visit
Expressed priorities on arrival
Implicit fatigue & energy reading
What the system surfaces
Context at the point of decision — and only then
Silence during exploration and discovery
A picture of conditions, not a recommended path
AI role: environmental interpreter, not recommendation engine
Reduce uncertainty,
not spontaneity.
Context before
recommendation.
More value in the zoo
than in the app.
Discovery
over checklists.
06 / Product Architecture

Information Architecture
+ Experience Layers

Layer 03
Decision Support Surfaces
Context cards at decision moments
Orientation state on arrival
Discovery-mode browsing
Quiet mode defaults
Layer 02
Visitor State Awareness
Group profile on entry
Remaining visit time
Priority & interest signals
Implicit fatigue reading
Layer 01
Contextual Intelligence
Real-time enclosure status
Crowd density model
Event & feeding schedule
Decision moment detection
Layer 00
Physical Environment
Zoo spatial structure
Animal enclosures & zones
Service touchpoints
Keeper knowledge system
In scope · MVP
Contextual card system at decision moments
Environmental awareness layer (crowd, activity)
Arrival orientation state
Discovery-mode browsing (no route pressure)
Out of scope · intentionally
Full route optimization engine
Social sharing & photo features
Gamification layer
Personalization over time

The MVP defines confidence, not completeness. Every excluded feature was tested against one question: does this help visitors feel ready to choose, or does it add noise?

Interface
07 / Prototype

The interface is the smallest part of the system.

Three screens demonstrate the full design logic: how the system establishes context, how it surfaces a relevant moment at a crossroads, and — crucially — how it stays quiet when it should. Restraint is a feature, not the absence of one.

10:14
Contextual Confidence · Arrival
Building your
visit context
Your visit today
2 adults · ~3 hours
Priority: African savanna, reptile house
Quiet
Crowds now
Active
Savanna
14:30
Next feeding
Context established
Savanna has high animal activity right now. Good time to start there.
"Good — that settles it. Let's go there first."
Context
Explore
Plan
S.01 · Arrival state
Context surfaced before the first decision — not after it.
Design logic: context before choice · not a recommendation
11:52
Decision moment · Penguin Enclosure
Is this worth
the walk?
Right now
12 minutes away · Moderate crowds
Animals: active in outdoor pool
Active
Animal status
Medium
Crowds
Time remaining
~1h 20min · Reptile house still on list
Both reachable today.
Active now — a good window
Context · Not a recommendation
"We don't want to miss the feeding again — let's go."
Context
Decide
Plan
S.02 · Decision moment
The system builds confidence — the visitor decides. No prescribed path.
Design logic: conditions + time remaining · visitor decides
13:08
Explore mode · Discovery active
Wander freely
System is quiet.
You're exploring.
🦁
African Savanna
Active now · High visibility
4 min
🐢
Reptile House
On your list · Quiet now
9 min
🦜
Tropical Birds
Not on list · Feeding at 13:30
nearby
"That moment made the whole visit worth it."
Context
Decide
Explore
S.03 · Explore mode
Serendipity preserved by design. Quiet is an active choice, not absence of design.
Design logic: restraint as a feature · discovery protected
Open Interactive Prototype

Navigate all three screens and test the quiet mode.

What makes this project worth your attention
AI Experience Design
Designed AI not as a recommender, but as an environmental interpreter. A conceptual shift that required reframing the brief entirely.
Spatial Intelligence
Architectural background applied to product design. Physical space treated as a design material, not a backdrop.
Research-Led Reframe
Original brief: navigation app. Field research revealed visitors needed confidence, not directions. Brief abandoned, better problem found.
Service Design Depth
Four-layer architecture from physical environment to decision surface. The interface is the smallest part — the system is the design.
08 / Impact & Learnings

What this work demonstrated.

Spatial thinking produces better product questions
An architectural background surfaces the physical environment as a design material — not just a backdrop. The zoo is a system of thresholds, territories, and movement. Designing for it requires that vocabulary.
"Once we stopped thinking about screens and started thinking about space, everything changed."
The real problem only appears when you're listening in the field
The entire design direction shifted in field session three. The brief was about navigation. Visitors were trying to tell us something else entirely. You only hear it if you're there.
"The moment of reframing didn't come from a workshop. It came from standing at a junction and watching a family turn back."
AI's best role in physical spaces is interpreter, not guide
Recommendation assumes the system knows better. Context assumes the visitor knows what they want — and gives them the information to act on it with confidence. The difference is everything.
"A companion that notices when a moment might be worth your attention — and then gets out of the way."
What I would do differently
Push earlier into physical environment testing. The most valuable insights came from being in the zoo. Six weeks of desk research could have been compressed into three field sessions that started on day one.
Test the serendipity hypothesis with families specifically. Parents with young children showed the most acute decision pressure. "The kids are getting tired — what's still worth seeing?" deserves its own design iteration.
Involve zoo staff earlier as co-designers. Keepers hold the real-time environmental knowledge the system needs. Their input from week one — as design partners, not just data sources — would have changed the service layer significantly.

"Designing for uncertainty means accepting that the best outcome is not the most efficient one."

End of journey
09 / Reflection

This project changed how I think about AI's role in physical environments — and about what it means to design for a place rather than a task.

Spatial thinking — the habit of reading environments as systems of movement, threshold, and territory — turns out to be exactly the right lens for designing AI experiences in places. Physical space has a grammar. Visitors read it instinctively. The design challenge is to speak that grammar fluently, not to replace it with a screen.

What this project clarified for me is the limit of optimization as a design goal. Optimization assumes we know what the visitor wants better than they do. Context assumes something different: that they already know what they want, but they're missing the information to act on it with confidence. One gives directions; the other gives ground to stand on.

There are things I didn't resolve. How does a system avoid becoming paternalistic when context becomes habit? I left them deliberately open — because the right answer depends on real visitors in a real zoo, not a design studio in Hamburg.

Designing for exploration means leaving some questions deliberately open.

Reframe Discover Build
← Previous project
AI Family Support
AI Design · Service Design
Next project →
TAKT
Renovation Planning · Product Design
Interested in the thinking behind this?
Open to Product Design, Experience Design, and AI Experience Design roles in Hamburg.
Get in touch