Technical Guides
Practical guides on CV, AI, and systems engineering
Getting Started with Edge CV Deployment
A step-by-step guide to deploying your first computer vision model on edge hardware. Covers hardware selection (Jetson Nano vs Orin vs Intel NUC), OS setup, model conversion to TensorRT, multi-stream video processing, and basic monitoring. Includes Docker templates and sample code for common detection tasks. Written for engineers with Python experience who are new to edge deployment.
Designing Camera Layouts for CV Projects
How to plan camera placement for reliable computer vision: field of view calculations, lens selection, mounting height and angle, lighting requirements, and network infrastructure. Includes templates for common scenarios: warehouse counting, perimeter monitoring, license plate capture, and retail analytics. Based on real layouts from our production deployments.
Building Reliable Integration Pipelines
Architecture patterns for connecting AI systems with enterprise software: message queues, event sourcing, idempotent APIs, retry strategies, and circuit breakers. Practical examples with 1C, SAP, and custom WMS systems. Covers monitoring, error handling, and recovery strategies for production environments where downtime costs money.