Back To Projects
Back To Projects

How SUPA helped a Mobile App Design company classify mobile screen interfaces into 117 different categories

SUPA helped a mobile app design company scale their interface database 16x in 3 months with 90% accuracy, freeing resources for AI innovation.

Problem statement

The Client needed to expand its mobile app screen and interface design database to 117 categories to train their AI model.

SOLUTION

SUPA’s highly-trained workforce and proprietary platform enabled a streamlined workflow that ensured consistent throughput while maintaining high-quality standards.

RESULT

The Client expanded and scaled up their database by 16 times within 3 months.

Overview

The Problem

The Client’s AI models rely on a vast database of mobile app screen and interface designs to generate innovative layouts for their customers. However, they faced a significant hurdle: their database contained 117 unique interface categories, each requiring meticulous classification.

With a high volume of interface images to process and classify, the Client’s internal annotating team quickly reached capacity constraints. This bottleneck not only slowed down their ability to expand their database but also diverted valuable resources away from refining their AI models.

The Client needed a solution that could:

  1. Scale their classification efforts without compromising accuracy.
  2. Relieve their internal team’s workload.
  3. Accelerate the growth of their interface design database.

The Solution

SUPA stepped in with a tailored solution designed to address the Client’s unique needs. Leveraging our highly-trained and experienced labeling workforce and proprietary platform, we implemented a streamlined workflow that included:

  • Efficient Data Classification: Our team meticulously classified interface images across all 117 categories, ensuring consistency and precision.
  • Quality Checks: Rigorous quality control measures were integrated into the workflow to maintain the Client’s high accuracy standards.
  • Scalable Throughput: Our platform enabled seamless coordination and consistent data throughput, allowing us to handle large volumes of work efficiently.

By partnering with SUPA, the Client was able to offload the classification workload, freeing up their internal team to focus on enhancing their AI models.

The Result

The impact of our collaboration was transformative. Within just 3 months, SUPA helped the Client:

  • Scale their classified interface design database by 16 times.
  • Achieve an impressive 90% accuracy rate, meeting and exceeding the Client’s agreed service standards.
  • Relieve internal capacity constraints, enabling the Client to reallocate resources to AI model refinement and innovation.

This partnership not only accelerated the Client’s database expansion but also empowered them to deliver even more value to their customers.

Why SUPA?

This project is a testament to SUPA’s commitment to delivering scalable, accurate, and efficient data solutions. Whether it’s classifying complex datasets, streamlining workflows, or enabling businesses to focus on their core strengths, we’re here to help our clients achieve their goals.

Our success with the Client highlights the power of collaboration and the importance of leveraging specialized expertise to overcome challenges and drive innovation.

Let’s transform your business together

If your business is facing similar challenges or looking to scale your data operations, SUPA is here to help. Let’s work together to unlock new possibilities and achieve your vision.

Other Projects

Discover the work we do

View All
View All

Data labeling for autonomous vehicle training

Discover how SUPA's specialized data labeling services enhanced an autonomous driving company's models, achieving 95% accuracy

problem
The client needed to enhance their autonomous driving model's accuracy in interpreting vector spaces to meet safety standards and effectively navigate diverse environments.
solution
SUPA provided expert data labeling services, combining rigorous annotator training and human-machine collaboration, along with a dedicated quality control team to ensure high-quality, consistent data.
result
SUPA continues to deliver labeled data to the Client with 95% accuracy, significantly improving the client’s model predictions and meeting tight delivery timelines since 2022.
95% accuracy
Autonomous Vehicles
Computer Vision
Data labeling for autonomous vehicle training

STEM Dataset

Bilingual Multimodal STEM Dataset — a curated collection of 500 Math and Physics questions in Malay and English, some enriched with relevant images.

problem
AI models often struggle with bilingual and multimodal STEM tasks due to a lack of high-quality, domain-specific datasets in languages like Malay and English.
solution
We created a curated dataset of 500 Math and Physics questions in Malay and English, complemented by a public leaderboard to benchmark AI model performance.
result
AI teams now have a reliable resource for fine-tuning and evaluating models on real-world STEM tasks, setting a new standard for bilingual and multimodal AI development.
500 high-quality Math and Physics questions
Evaluation Leaderboard
STEM-focused AI evaluation
STEM Dataset