⚙️🦀 Build modular and scalable LLM Applications in Rust

🚀 Build AI Agent Teams as Production-Ready APIs. Orchestrate CrewAI agents with FastAPI for enterprise-grade AI services. Leverage Groq's lightning-fast LLMs to deploy collaborative AI workflows at scale.

Agentic AI Agents Factory Orchestrator modular, and asynchronous AI agent factory designed for AI-made dynamic workflow orchestration using LLM integration. Build scalable, agentic automation pipelines with robust error handling, plugin-based capabilities, a concurrent operations or outsource workflow building to Operator Agent.

PyroScan AI is a GenAI-powered multi-agent system for real-time forest fire prediction across 10 global zones. Designed for researchers and disaster teams, it includes agents for fire detection, weather analysis, and historical data mining. Deployable via CLI or Docker.

ASAN: A conceptual architecture for a self-creating (autopoietic), energy-efficient, and governable multi-agent AI system.

A lightweight Explainable AI CNN for PathMNIST medical imaging, achieving 91%+ accuracy with Integrated Gradients and SQLite-based attribution storage. Built in PyTorch, this scalable model delivers high performance, transparency, and real-world readiness, making it ideal for medical AI, edge deployment, and explainable deep learning research.

MedMNIST-EdgeAI -> an end-to-end exploration into model distillation, optimization, and deployment for resource-constrained environments, all centered around the MedMNIST medical imaging dataset.

HAG-MoE introduces a revolutionary approach to artificial intelligence by combining the power of Transformer attention mechanisms with hierarchical Mixture of Experts architecture

🔍 Enhance medical imaging with a lightweight CNN model that offers over 91% accuracy and integrated explainability for better clinical trust.

An enterprise-grade AI-native platform engineered for cognitive systems orchestration, autonomous workflows, and scalable infrastructure. Integrates intelligent agents, real-time data pipelines, and adaptive architectures to transform fragmented tools into unified systems, delivering performance, resilience, and up to 85% cost efficiency.

Governed AI-assisted decision workflow for regulatory authoring with validation, review orchestration, and auditability

Temporal forecasting architecture: strategic decomposition combining TabNet feature extraction, cross-interaction layers, and interpretable seasonal/regressor components for multi-horizon predictions.

Interpretable forecasting architecture: strategic hierarchical decomposition with attention mechanisms for explainable temporal predictions across time series domains.

Coordinate multiple AI coding agents from a single interface to streamline development workflows.

Decision confidence architecture: strategic uncertainty quantification for autonomous routing between classification, LLM review, and human intervention across visual inspection workflows.