Multi Zone Configuration

redesigning how operators scale spatial intelligence

in computer vision AI pipelines

Product design internship at Airbus

Domain

Computer Vision AI, Enterprise Analytics

Duration

December 2025 - January 2026

Team

Analytics & AI Division at Airbus

Product

Enterprise Computer Vision

platform for Airbus

Impact tracked over two months post-launch

32%

increase in multi-zone pipelines created

46%

active pipelines now use this feature, from 14% previously

3.3x

growth in feature adoption recorded

*data recorded as of April 2026

My contributions & solution

I designed the experience of:

helping operators define areas of interest in camera feeds

and configuring analysis models for those zones

I also:

built production ready prototypes using claude code

rapidly tested & iterated for quick user feedback

Onto the problem I solved

earlier,

users could only define only one zone

but now,

they could start configuring up to five

Usability issues were

hindering the adoption of this feature

no affordance that users could draw multiple zones

no cue that zones could be configured individually

object-level customization wasn’t implemented

Previous user research insights helped me establish constraints

1

The user group varies in technical experience and expectations, ranging from low-code to highly technical

2

Stakeholders can articulate pipeline outcomes,

but rely on other teams to translate those goals into solutions

Constraints established earlier by these insights

The solution needed to be templated enough for non-power users to ghost-drive the pipeline through

/ phrasing the problem

how might we

help users easily define multiple zones and configure models at an object level in them?

Idea one:

Progressive panels for multiple levels of filtering

Tradeoffs in this approach:

Supports the granularity in customization required, and made zone creation more discoverable

Excessive nesting depth causing cognitive load, too many user goals in a focused UX pattern (modal)

An exploration: there’s so many actions, how do we fit them in a single modal?

Idea two:

Splitting the task goals into different nodes

Tradeoffs in this approach:

Separation of concerns, a granular pipeline is easier to understand and debug

The user is required to manually place and connect model configuration nodes

The two user goals are split into two steps now, visually mapped on the pipeline canvas

feedback from users after testing:

no clear design won! users pointed out things working and ones that weren’t

What users preferred

Users preferred the complex multi-panel approach because they could preview the zone while configuring models

What users pointed out

non-power users found it tough to build their own pipeline

and add workflow blocks

Idea three:

bringing feedback from the previous iterations together

Tradeoffs in this approach:

Previews show how data flows through the pipeline, making it easier for non-power users

Tabs in the side drawer might not scale if we add more models in the future

I finally prioritized across solutions

by evaluating them against the design goals from research

The Final User Flow

Why this solution works:

A node-level preview of the zones helps users visually track how information flows through the pipeline

Each zone auto-maps to an event analysis node by default, catering to the template model

Placing all model options together saves the user the effort of building this part ground up

Learnings & Conclusions

This was but a snapshot of my process, let’s connect to take a deeper dive!

The change in perspective question framing brings

My ideas radically improved when I shifted from designing for tasks to designing for outcomes.

Put the bad ideas on the canvas too

Though initially hesitant, bad ideas helped me define creative boundaries & find unobvious feedback.

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