01
Rapid In-House Prototyping
Evaluating lightweight, custom model architectures locally to rapidly benchmark novel layout tokenizers and UI intent parsers before scaling compute.
FocusIteration velocity
Crux AI Research & Development
Pioneering Domain-Specific Generative UI Models
We bridge foundational AI research with production-grade web applications through custom model training, proprietary dataset engineering, and specialized UI generation pipelines.
Research pipeline
Interface intelligence
Active R&D
Crux
UI
MODEL
Core research pillars
From local experiments to production intelligence.
Our research process joins rapid architecture exploration, scaled training, and deeply specialized data work into one focused generative UI program.
02
Scaled Multi-GPU Fine-Tuning
Training specialized domain models on dedicated GPU clusters to generate production-ready HTML, Tailwind CSS, and interactive component code.
FocusSpecialized training
03
Domain-Specific Data Engineering
Curating and structuring vast collections of web layouts and design systems with specialized loss functions focused purely on code syntax and visual fidelity.
FocusHigh-signal datasets
Proprietary R&D initiatives
Purpose-built models.
Distinct operating scales.
Our research portfolio pairs a high-capacity generation engine with a lightweight core for responsive design understanding.
R&D / 01
Specialized UI Models
Crux Generation Engine
Multi-billion parameter models fine-tuned specifically on modern frontend frameworks, optimized for low-latency generation and long-context UI layout packing.
- Modern framework fluency
- Long-context layout packing
- Production-oriented output
R&D / 02
Real-Time Design Intelligence
Crux Lightweight Core
In-house model prototyping for ultra-fast, real-time client-side layout previewing and design intent parsing.
- Low-latency inference
- Immediate layout previewing
- Design intent interpretation
The data flywheel
Real use sharpens every benchmark.
Real-world generation data and user interactions on our platform feed directly back into our research pipeline. This creates a continuous, carefully evaluated loop: practical requests reveal where models succeed, structured reviews expose opportunities, and new training passes improve both visual quality and code reliability.
By connecting research to production, our benchmarks stay grounded in the interfaces people actually want to build—not isolated demonstrations.
- 01GenerateModels create real interface candidates.
- 02ObserveQuality signals reveal practical outcomes.
- 03EvaluateBenchmarks measure code and visual fidelity.
- 04ImproveCurated learnings guide the next training pass.
Research in production
Build with Next-Generation AI UI Models
Move from an idea to a working interface with the Crux platform.