Ayrshire AI Model Optimizer: AI Model Optimization for Industrial Deployment

HOLSTEIN: Data Management Platform Built for Manufacturing Environments

HOLSTEIN Manufacturing Data Platform Overview HOLSTEIN is a manufacturing data platform designed to systematically manage and accumulate inspection and production data generated on the factory floor. It organizes inspection data, defect information, process data, equipment information, and engineering know-how generated throughout projects within a consistent structure, improving both data reusability and long-term value. --- Key Features Manufacturing Data Management HOLSTEIN provides centralized management of various types of manufacturing data. Product information management Process information management Inspection data management Defect data management Equipment information management Project history management Inspection Know-How Management Engineering experience and know-how generated during projects can be accumulated and managed as reusable data assets. Inspection condition management Optimization history management Defect analysis records Troubleshooting case management Operational know-how management Data Standardization HOLSTEIN standardizes information that would otherwise remain fragmented across individual projects, creating a consistent data management environment. This can provide benefits such as: Shorter deployment time Improved engineering efficiency More consistent inspection quality Reuse of existing project assets Continuous Data Asset Development Inspection systems generate more data as they continue to operate. HOLSTEIN systematically accumulates this data and transforms it into a digital asset that can contribute to greater manufacturing competitiveness over time. --- Benefits Faster Deployment Data and knowledge from previous projects can be reused to reduce the time required to deploy new projects. Improved Quality Accumulated data can be used to establish more stable and consistent inspection environments. Preservation of Engineering Know-How Experience and technical knowledge can be managed as structured data, helping preserve them as long-term organizational assets. Continuous Improvement As the number of projects grows, the accumulated data assets also expand, enabling greater productivity and operational efficiency. --- Vision Manufacturing competitiveness starts with data. HOLSTEIN continuously accumulates manufacturing data and field experience to create faster, more accurate, and more efficient manufacturing environments.

RANI: Edge AI Inspection Platform for On-Site Processing Without a Server

From Camera Acquisition to AI Inference, Rule Inspection, PLC Communication, and Data Logging — All in One System When introducing AI inspection into a manufacturing environment, simply running an AI model is not enough. The entire inspection workflow must operate reliably within a single system: acquiring images from cameras, analyzing defects with AI, evaluating inspection conditions using rule-based logic, transmitting results to the PLC, and recording inspection data. RANI (Real-time AI Networked Inspection) is BISON’s Edge Vision Framework designed specifically for these industrial inspection environments. RANI is optimized for NVIDIA Jetson-based edge devices, enabling image acquisition, AI inference, inspection judgment, and PLC output directly on-site without requiring a separate AI inference server. Why an Edge AI Inspection Platform Is Needed In manufacturing environments, AI inspection depends heavily on inspection speed, equipment integration, and network reliability. A server-based architecture that sends images to an external server for AI inference can increase system complexity and may introduce latency or network dependency depending on the production environment. RANI is designed for NVIDIA Jetson-based edge devices, allowing image acquisition, AI inference, inspection judgment, and PLC output to be handled directly on-site without a separate inference server. By leveraging Jetson-based GPU acceleration, RANI can build real-time inspection pipelines even on compact edge devices, enabling fast inspection decisions close to the production line. Core Inspection System: Integrating AI and Rule-Based Inspection in a Single Workflow RANI provides an AI-powered inspection pipeline designed to operate on compact edge devices. The system connects camera integration, AI inference, rule-based inspection, PLC control, and data logging into a unified workflow. Industrial camera image acquisition AI-based inspection Rule-based inspection AI + Rule hybrid inspection PLC-based inspection cycle control Real-time inspection result processing This enables the implementation of a complete industrial inspection system for real production environments, rather than simply running an AI model in isolation. Camera & Device Integration: From Triggered Capture to PLC Control RANI provides camera and automation equipment integration features designed for industrial production lines. Basler GigE camera integration Extensible architecture for various industrial cameras Soft Trigger support Continuous Acquisition support PLC-based inspection cycle control OK / NG result output When an inspection is initiated through a PLC or I/O trigger, the full sequence — image capture, AI inference, rule-based inspection, result judgment, and PLC output — is processed in real time. AI + Rule Hybrid Inspection: Combining AI Flexibility with Rule-Based Consistency In industrial inspection, it is often more effective to combine AI with rule-based logic rather than relying on AI alone for every decision. AI models are particularly effective at detecting complex patterns and irregular defects, while rule-based inspection is well suited for clearly defined criteria such as position, dimensions, regions, and threshold conditions. RANI allows AI inspection functions such as Object Detection, Segmentation, Classification, and QR Code Detection to be combined with rule-based inspection logic within a single workflow. This approach combines the flexibility of AI with the consistency of rule-based inspection, helping improve both inspection accuracy and decision reliability. Inspection Monitoring: View Inspection Status and Result Images in Real Time RANI provides real-time monitoring capabilities even in edge environments, allowing inspection results to be reviewed and analyzed directly on-site. Real-time inspection result display Inspection status monitoring Inspection image review ROI-based result visualization ROI overlay display Operators and quality personnel can immediately review inspection status and result images, making AI and rule-based inspection outcomes easier to understand visually. Logging System: Recording Inspection Data for Quality Analysis and Traceability RANI includes a lightweight asynchronous logging system designed to operate reliably on edge devices. Supported Log Formats CSV Image PNG / JPG / BMP Recorded Data Inspection results AI inference results ROI information Inspection time Inspection images Stored inspection data and images can be used for quality analysis, traceability, defect root-cause investigation, and customer response documentation. Core Technology: Key Technologies for Jetson-Based Edge AI Inspection | Category | Description | | ------------------ | -------------------------------------------------------- | | Core Engine | High-performance C++-based vision processing engine | | AI Inference | GPU-accelerated AI inference engine | | Vision Processing | Optimized industrial image processing pipeline | | Device Integration | Industrial camera and automation device integration | | Platform Support | NVIDIA Jetson, Edge AI platforms, and Linux environments | | System Logging | Inspection data and image logging system | RANI is built on a high-performance C++ vision processing engine and a GPU-accelerated AI inference architecture. Designed for NVIDIA Jetson and Linux-based edge environments, it enables AI and rule-based inspection to run together even in compact industrial equipment and embedded inspection systems. System Architecture: Modular Integration of Core Inspection Functions RANI Edge Vision Framework consists of the following core modules. Application Layer Inspection system UI and user interface Inspection Pipeline Manages the processing flow from image acquisition to inspection result generation Device Interface Connects cameras, PLCs, lighting systems, and other industrial devices Vision Tools Provides AI-based inspection and rule-based vision algorithms System Infrastructure Handles configuration management, data I/O, and logging Each module can be configured flexibly according to the production environment and inspection requirements, providing the core functions needed for edge device-based inspection systems within a single framework. Inspection Flow: Real-Time Inspection Pipeline from Trigger to Image Save  Starting from a PLC or I/O trigger, RANI processes the entire inspection flow — image acquisition, AI inference, vision processing, result judgment, PLC output, UI update, logging, and image storage — within a single real-time pipeline. --- Design Goals: A Stable Industrial Inspection Platform for Compact Edge Devices RANI was designed around the following goals: Edge AI inspection without a separate AI inference server Optimization for NVIDIA Jetson-based Edge AI environments Lightweight platform suitable for compact equipment Support for high-speed inspection cycles Integrated AI + Rule-based machine vision architecture Modular architecture connecting cameras, PLCs, lighting, and logging systems Conclusion RANI goes beyond simply running AI models. It is BISON’s Edge Vision Framework designed to process the entire inspection workflow directly on an edge device in manufacturing environments. In NVIDIA Jetson-based Edge AI environments, RANI integrates camera image acquisition, AI inference, rule-based inspection, PLC communication, and inspection data logging into a single system, providing an inspection environment optimized for compact industrial equipment and inline production lines.

TITAN Vision Platform: Machine Vision Framework Optimized for Industrial Environments

Industrial-Grade AI Vision Platform for High-Performance Inspection Systems TITAN Vision Platform is a machine vision framework optimized for industrial environments. Built on a real-time inspection pipeline and a high-speed image processing engine, TITAN enables the development of reliable industrial inspection systems. It supports multi-camera inspection environments and combines AI-based inspection with rule-based vision algorithms, providing high accuracy and flexibility across a wide range of industrial processes. System Architecture  --- Core Modules Camera Manager Responsible for industrial camera control and image acquisition. Key features: GigE Vision camera support Hardware trigger synchronization Simultaneous multi-camera acquisition High-speed frame capture Buffer management --- Image Processing Pipeline Handles real-time frame processing and the overall inspection flow. Pipeline stages: Image acquisition Image preprocessing ROI extraction Inspection processing Result generation The pipeline is optimized for low-latency, real-time inspection. --- GAUR Inspection Engine  The core inspection engine of the BISON Vision Framework. GAUR is named after the Gaur (Bos gaurus), one of the world’s most powerful wild cattle species. Design principles: Powerful processing performance High-speed inspection High reliability in industrial environments GAUR AI Inference Engine Provides deep learning-based inspection capabilities. Supported functions: Object Detection Classification Segmentation Anomaly Detection OCR Optimized inference backends can be used for high-performance AI processing. Examples: TensorRT Custom C++ Inference Runtime --- Visualization UI A real-time monitoring interface for inspection systems. Features: Real-time camera view Inspection result overlays ROI configuration Production status monitoring System status display --- Production Logging System Records inspection results and system events. Logged data includes: Inspection results OK / NG statistics Cycle time Camera events System logs This enables production traceability and quality analysis. --- Software Architecture  --- Performance Architecture TITAN uses the following technologies to support high-speed production lines: Multithreaded processing Asynchronous image pipelines Ring buffer memory management Parallel inspection execution Non-blocking UI updates --- Key Machine Vision Applications Surface defect inspection Assembly verification Dimensional measurement Product classification Barcode recognition Presence / absence inspection --- Example Inspection Workflow  --- Supported Hardware Typical system configuration: Industrial PC Intel CPU NVIDIA GPU (Optional) GigE Industrial Camera Industrial Lighting PLC Interface --- Design Philosophy The BISON Vision Framework was developed around the following goals: Reliability in industrial environments Real-time processing performance Modular architecture Scalability for AI-based inspection Multi-camera scalability