Our Story

The Beginning

ShoeLabs LLC was founded in November 2023 in New York by a team of ML researchers and footwear designers. We saw a fundamental gap in the design industry—traditional CAD workflows required specialized training and weeks per iteration. Generative models had demonstrated breakthrough capabilities in other creative domains, but footwear design presented unique challenges: maintaining manufacturing constraints, ensuring material feasibility, and generating production-ready 3D geometry.

Our first breakthrough came during initial research: successfully training diffusion models conditioned on manufacturing constraints. Within three months of founding, we developed our core architecture—combining latent diffusion for high-resolution generation with neural implicit representations for 3D mesh reconstruction. Early validation with independent designers showed 10x reduction in iteration time compared to traditional CAD workflows.

The Technology

ShoeLabs operates on a multi-stage ML pipeline. Input encoding uses transformer-based architectures with cross-modal attention mechanisms to extract semantic features from text and visual data. Our generation stage employs latent diffusion models—denoising diffusion probabilistic models operating in compressed latent space trained on 100,000+ annotated footwear images.

The core innovation is our 3D reconstruction system. We developed neural implicit representations using signed distance functions (SDFs) and occupancy networks to generate watertight meshes. Differentiable rendering enables end-to-end training on 2D supervision while maintaining 3D geometric consistency. The system enforces manufacturing constraints through custom loss functions that penalize non-manufacturable geometry during training.

For domain adaptation, we employ few-shot learning and meta-learning protocols. Clients can fine-tune models on proprietary design libraries with minimal data (50-100 examples). Our style encoder learns brand-specific embeddings through contrastive learning objectives, enabling consistent aesthetic output without retraining the entire generation pipeline.

Model selection uses ensemble methods—a mixture-of-experts architecture routes inputs through specialized sub-networks based on design complexity. Final outputs combine predictions from multiple models weighted by confidence scoring and geometric feasibility metrics. This approach achieves 94% accuracy in generating manufacturing-feasible designs.

Our Mission

We believe that great design shouldn't be limited by budget or technical skills. ShoeLabs democratizes sneaker design by putting professional-grade AI tools in the hands of anyone with a vision—from solo creators to established brands.

Our platform has evolved significantly since launch. What began as a simple text-to-image tool has grown into a comprehensive design studio with 3D modeling, PBR material rendering, brand DNA training, and marketplace integration. Today, over 1,500 designers and brands trust ShoeLabs to bring their ideas to life.

The Team

We're a team of 6 based in New York. Our backgrounds span computer vision, computational geometry, machine learning systems, and footwear design. The technical team includes researchers with publications at NeurIPS, CVPR, ICCV, and SIGGRAPH. Design advisors bring 50+ combined years of experience from major athletic footwear brands.

ShoeLabs is self-funded and profitable. Our advisory board includes ML researchers specializing in generative models, 3D computer vision, and manufacturing-constrained design optimization.

Our Values

Innovation drives everything we do. We're constantly pushing the boundaries of what's possible with AI and 3D design, exploring new architectures and training methodologies to improve output quality.

We believe professional tools shouldn't cost a fortune. ShoeLabs is priced to be accessible to independent creators, students, and small brands without sacrificing capabilities.

Beyond building software, we're building a community of designers who inspire and support each other. Every design created on our platform meets professional production standards.

Infrastructure

ShoeLabs operates on distributed GPU infrastructure across cloud providers with auto-scaling capabilities. Inference runs on clusters of NVIDIA A100 GPUs with optimized CUDA kernels for sub-60-second generation latency. Production system handles 10,000+ API requests daily with 99.9% uptime SLA.

Training infrastructure includes continuous learning pipelines with automated data curation, model evaluation, and deployment. Base models undergo quarterly retraining incorporating new designs and user feedback. Model versioning and A/B testing infrastructure enables safe deployment of incremental improvements. All training data is ethically sourced with proper licensing protocols.

Timeline