Built for Performance.
Designed for Privacy.
Explore the cutting-edge technology powering ModelIQ's hybrid MLOps ecosystem. We unify AutoPilot automation with Human-in-the-loop control to bring enterprise-grade data science directly to the browser via cloud orchestration.
The Local Intelligence Stack
Traditional machine learning platforms require expensive cloud GPU clusters and complex API calls. ModelIQ redefines this by performing all heavy computations directly on the user's hardware.
WebAssembly Acceleration
We compile complex model training logic into WebAssembly (WASM), delivering near-native performance for tasks like Gradient Boosting and SVC.
Zero-Server Computation
By utilizing the user's browser as the training environment, we ensure absolute data privacy. Raw datasets never touch a third-party server.
Frontend
Next.js 14
React framework for high-performance server-side rendering and routing.
ML Engine
Scikit-JS
Native browser execution of Scikit-Learn style algorithms.
Neural Core
TensorFlow.js
Powerful library for training and deploying ML models in JavaScript.
Styling
Tailwind CSS
Utility-first CSS framework for modern, responsive UI design.
Infrastructure
Firebase
Serverless authentication and real-time database synchronization.
Persistence
IndexedDB
Local storage engine for handling large-scale datasets and model states.
The Future is Decentralized
Edge-First
Train and deploy where the data lives.
Real-Time
Zero network latency during inference.
Scalable
Infinite scale through user-driven compute.
Ready to build?
Join the growing community of developers building the next generation of privacy-preserving machine learning applications.