Method AI: Crafting an AI-Powered Recycling Assistant for Modern Workplaces

Designed and launched Method AI—an intelligent, location-aware recycling assistant that reduced office waste contamination by 19 percent and laid the groundwork for Method Recycling’s digital product ecosystem across New Zealand and Australia.

What I built

• Multi-platform solution: iOS mobile app, web admin dashboard, and robust backend.
• Real-time, AI-powered guidance for recycling at work—integrated with Method’s physical bins and branding.
• Beta tested in ten workplaces, supporting facility managers and thousands of office workers.

My role and tools

Role: lead product designer (ownership across strategy, research, user experience, UI, and systems integration from launch to beta delivery)

Tools used:

• Figma: for design systems, wireframing, and hi-fi prototyping
• Xcode, Cursor: design-engineering handoff and app implementation
• Supabase: backend for AI RAG knowledge system and data storage
• CloudKit: iOS data synchronization
• Vercel: web deployment
• GitHub: design and code version control

The challenge

Despite clear visual guides, Method Recycling’s bins and signage could not eliminate confusion—34 percent contamination rates exposed the need for smarter support. Static instructions failed to adapt to region-specific recycling rules, and busy employees made errors at the point of disposal.

Process overview

1. Problem and research

• Analyzed IoT bin data and identified peak confusion during lunch and snack breaks; contamination baseline was 34 percent.
• Conducted user interviews and job shadowing to map pain points across three core office personas: busy executives, sustainability champions, and conscientious employees.
• Key insight: there is a three-second window to influence confident recycling choices at the bin.

2. Design and prototyping

• Camera-first workflow: users receive point-and-dispose recommendations quickly, minimizing friction.
• Progressive information: users get the “just right” answer fast, with the option for detailed information and local council rules.
• Contextual guidance: AI adapts advice based on office location and specific council regulations.
• Admin dashboard: facility managers and Method staff can track results, adjust council guidelines, and review performance analytics.

3. Technical architecture

A layered, modular system ensures speed, adaptability, and scalability across business environments.

Results and impact

• 19 percent reduction in contamination rates compared to pre-app baselines.
• 78 percent weekly retention among beta users.
• 92 percent correct identification on scanned items; 96 percent contextual accuracy.
• 63 percent drop in AI processing costs using smart workplace caching, making the platform scalable for business rollout.
• Established the technical and UI/UX foundation for further IoT bin integration and corporate sustainability reporting.

Key learnings

• Ecosystem mindset wins: by tightly integrating the AI with bins and signage, the experience felt native, accelerating adoption and confidence.
• Context matters: the RAG knowledge system built trust by delivering local, council-specific advice instead of one-size-fits-all instructions.
• User behavior is shaped by moments: the three-second response target was not just technical—it aligned with real workplace habits.
• Design for both people and organizations: the dashboard and admin tools empowered both end users and facility managers, which is crucial for business product success.

Takeaway

By understanding office behavior, leveraging AI for location-specific confidence, and keeping everything fast, I transformed recycling from a source of confusion into a moment of easy, positive action—setting up Method Recycling for more impact and future digital growth.