Niloom

Niloom AI. Redesigning a GenAI Platform for Spatial Creators

New York Startup. a generative AI platform that empowers spatial (AR/VR/XR) creators to build immersive worlds with ease.

Scope

Multi-platform system (Kiosk, Mobile, Web, Kitchen Displays, POS)

Product Design

Aug 2023 - Late 2024

See the live project

Project Overview

Client

Niloom AI

Industry

Generative AI · Spatial Computing · Creator Tools

Niloom AI is a next‑generation GenAI platform built for spatial computing. It enables creators to generate 3D scenes, interactive environments, characters, and narratives for AR/VR experiences. all from a browser.

My Role

As the co-Lead UX Designer, I collaborated closely with other UX designers, product creatives, and engineers to reimagine Niloom AI’s web platform. The mission was to transform a powerful but complex GenAI system into an intuitive, gamified, and production‑ready experience for spatial creators. without sacrificing flexibility or creative depth.

Core Challenge

How do you design a GenAI platform that is both powerful and playful. without intimidating creators?

Key challenges included:

- Translating abstract AI concepts into understandable UI patterns- Hard-to-find categories and meals
- Balancing creative freedom with guided workflows
- Designing for both beginners and advanced spatial creators
- Creating a system that could evolve rapidly alongside AI model updates

Process/Methodology

I approached the kiosk redesign by grounding every decision in usability, speed, and operational impact:

1. User-Centered Observation: Studied real customers using the kiosk during peak hours to understand navigation issues, customization confusion, and decision bottlenecks.

2. Flow Redesign: Simplified category structure, streamlined customization, and introduced a faster checkout path optimized for rush-hour efficiency.

3. Touch-Friendly UI/UX: Designed a clean, visual, tap-friendly interface with clear hierarchy, large touch targets, and high visibility for add-ons.

4. Upsell & Efficiency Optimization: Improved add-on discovery, reduced cognitive load, and created a more predictable step-by-step flow.

5. Iterative Testing: Validated prototypes with customers and staff, refining layout, speed, and clarity before piloting in live restaurant environments.

Results/Outcomes

The project delivered measurable improvements, including:

• Faster Ordering Flow: Average order time decreased by 32%, driven by clearer navigation, simplified customization, and improved accessibility.

• Higher Order Value: Add-on and upgrade selections increased by 15% after surfacing toppings and extras at the right moments

• Improved Operational Efficiency: Restaurants processed 27% more orders per hour, significantly easing peak-time congestion.

• Lower Staff Burden: Staff intervention at kiosks dropped by 40%, allowing teams to stay focused on service and kitchen operations.

• More Accurate Kitchen Order: Unified UX patterns with the POS reduced order errors by 18%, leading to smoother kitchen throughput.

• Positive Customer Feedback: Users consistently described the new kiosk as faster, clearer, and easier to navigate, boosting overall satisfaction.