Explore

Home
About
Services
Portfolio
Contact US
info@proscalify.com

Navid E-waste

Al-Powered E-Waste Recycling For A Greener Future

The Challenge

The primary challenge was to create a solution that not only aligned with the client’s vision but also stood out in a competitive market. We needed to combine creativity with functionality to deliver a design that resonated with the target audience.

navid-poster

overview

Our approach focused on developing a modern and professional identity that reflects the client’s values. By blending clean design, strategic messaging, and a user-centered perspective, we delivered a cohesive brand experience that enhances both visibility and credibility.

Logo Concept

navid-concept
navid-logo

Font

Poppins

Aa

#0b6e4f

#c5d86d

#f05d23

Social Media

We designed a consistent social media presence by creating templates, branded visuals, and content guidelines. This ensured that every post supported the overall brand identity while engaging effectively with the online audience.

navid-post-06
navid-post-05
navid-post-04
navid-post-03
navid-post-02
navid-post-01

Stationery Set

A professional stationery set was developed including business cards, letterheads, and other brand assets. The goal was to maintain a consistent look across all physical touchpoints, reinforcing brand recognition and trust.

MVP: AI-Powered E-Waste Image Recognition

Our MVP demonstrates how artificial intelligence can identify and classify electronic components from images. Users can upload or link a photo, and the system automatically detects key parts such as circuit boards, batteries, and connectors — paving the way for smarter and more efficient e-waste recycling.

How It Works

Image Input

Users upload or link an image of an electronic device or circuit

A trained computer vision model analyzes the image and locates recognizable components.

The system classifies elements like PCBs, wires, batteries, and casings

It provides metadata about potential materials and recycling categories, helping to optimize the sorting process

Challenges & Learnings

Data Diversity

Electronic waste varies widely in shape, texture, and color. Collecting enough diverse images for accurate AI training was a key hurdle.

Differentiating between visually similar components (e.g., chips vs. connectors) required iterative fine-tuning and synthetic data generation.

Processing high-resolution images efficiently demanded model optimization and compression without sacrificing accuracy

Lighting, background noise, and damaged devices affected recognition performance — leading us to enhance pre-processing and noise reduction.

Ensuring that the model could handle large datasets and future real-time detection needs was critical for long-term deployment

Insights & Resources

More
Scroll to Top