Specialized consulting in foundation models, computer vision, reinforcement learning, and edge AI — built on a decade of research and real-world deployment across robotics, retail, gaming, and industrial AI.
From architecture design and large-scale training to edge deployment — we own the full stack so you don't have to.
Fine-tuning and training VLMs and large multimodal models for your specific domain. We close the gap between general-purpose APIs and production-grade, task-specific performance.
Production-ready CV pipelines — object detection, segmentation, tracking, multi-camera geometry, and 3D understanding. Deep experience across robotics, retail, and industrial settings.
Designing and training RL agents for complex sequential decision-making — from robotic manipulation and autonomous systems to game AI. Covers agent architecture, reward design, sim-to-real transfer, and training infrastructure.
Bringing large models to resource-constrained hardware. Quantization, pruning, distillation, and TensorRT optimization for real-time, low-power inference on edge devices.
Architecture review, MLOps setup, and technical advisory. We help teams build ML systems that are reproducible, maintainable, and built to last — without accumulating technical debt.
Rigor AI was founded by Youssef Zaky, a principal machine learning engineer with over a decade of experience building AI systems that ship. His work spans computer vision, reinforcement learning, and foundation models across robotics, retail, broadcasting, gaming, and industrial automation — from foundational research to deploying real-time systems in live production environments.
He has led engineering teams from the ground up, scaling ML organizations from zero to production, and has hands-on depth at every layer of the stack — from novel architecture design and large-scale training to optimized edge deployment.
The name reflects the approach: rigorous engineering, not prototypes. Every model is built with production constraints in mind — latency budgets, data quality, deployment environments, and long-term maintainability.
A sample of production engagements delivered through Rigor AI.
End-to-end VLM pipeline for semantic image understanding and product recommendation — trained and fine-tuned at scale, with low-latency edge deployment for real-time inference.
Advisory engagement with a major game studio on the architecture and training of a reinforcement learning agent for a new title. Work covered agent design, environment structure, reward shaping, and training infrastructure to meet the constraints of a game development pipeline.
Practical perspectives on ML engineering, foundation models, and production AI systems.
Benchmarking, testing, reproducibility, maintainability — the practices that separate production ML from research code. Why rigor isn't optional when models run in the real world.
Read more →Whether you're looking for a contract engagement, architecture advisory, or a full project build — let's talk.