Our Work
Selected projects showcasing our approach to design and development.
Case Study
Vision Transformers for Small Datasets
Vision Transformers (ViT) typically require massive datasets to outperform CNNs. We propose a novel regularization techn...
Case Study
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce t...
Case Study
High-Fidelity Image Generation with Latent Diffusion Models
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DM...
Case Study
E-commerce Platform Migration
Reduced page load times and improved conversion rates through modern architecture and optimized data flows.
Case Study
SaaS Dashboard Analytics Redesign
Improved activation rate by simplifying onboarding flows and making key metrics more accessible to users.
Case Study
Fintech Marketing Website
Increased organic traffic and lead quality with a performance-focused site that ranks well and converts visitors.
Case Study
Understanding Context in Large Language Models
We investigate how large language models (LLMs) utilize context in long-document understanding tasks. Our analysis revea...
Case Study
Design System Foundation
Accelerated feature development and improved consistency across products with a comprehensive component library.
Case Study
Healthcare App Redesign
Enhanced user engagement and task completion rates by redesigning core workflows based on user research.
Case Study
Startup MVP Platform
Delivered a scalable MVP in record time, enabling rapid user testing and iterative feature development.
Case Study
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations fro...
Case Study
Attention is all you need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include a...
Case Study
Deep residual learning for image recognition
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of net...
Case Study
Generative Adversarial Nets
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train...