TDSE List of Products and Services

We will introduce a case where production planning automation was achieved in the manufacturing industry. There was a challenge to optimize "which products to produce at which factories and in what quantities," reducing the man-hours required for production site allocation by implementing an optimization model, while also considering production and transportation costs as well as the capacity of each factory. Therefore, we introduced a production planning optimization model. We formulated a mathematical optimization problem based on constraints such as product production costs, transportation costs, tariffs, and the capacity of each factory, and implemented it. [Case Overview] ■ Industry: Manufacturing ■ Business: Production Planning ■ Challenge: Mathematical Optimization ■ Analytics and AI Solution - Formulated and implemented a mathematical optimization problem *For more details, please refer to the PDF document or feel free to contact us. - Related link - https://www.tdse.jp/

• Other production management systems

We would like to introduce a case study of implementing automated document reading (AI-OCR) in the transportation industry. The company faced the challenge of wanting to reduce manpower in sorting the types of slips sent by shippers and inputting information (such as address, phone number, product number, etc.). Therefore, they created a model that retrieves text information from images using an OCR engine and outputs the corresponding text for receipt types and each item. This led to improved operational efficiency in slip sorting. 【Case Overview】 ■Industry: Transportation ■Business: Receipt sorting operations ■Challenges: Labor reduction, automation, AI-OCR ■Analytics and AI Solutions - Retrieve text information from images using an OCR engine - Create a model that outputs the corresponding text for receipt types and each item *For more details, please refer to the PDF document or feel free to contact us.

• Document and Data Management

We would like to introduce a case study of implementing regulatory classification AI (important document determination model) in the manufacturing industry. The company faced the challenge of wanting to streamline the extraction of information that impacts their operations regarding the constantly updated environmental regulations using natural language processing. To address this, they trained the AI on classification examples from past expert meetings, allowing them to remove documents that could be clearly deemed unnecessary from the updated information. They proposed a process where only the remaining documents would be assessed using traditional methods, leading to a reduction in the man-hours required for document classification decisions. [Case Overview] ■ Industry: Manufacturing ■ Business: Environmental Regulations ■ Challenge: Natural Language Processing, Labor Reduction ■ Analytics & AI Solution - Trained the AI on classification examples from expert meetings to remove documents that could be clearly deemed unnecessary from the updated information. *For more details, please refer to the PDF materials or feel free to contact us.

• Document and Data Management

We would like to introduce a case study on improving maintenance operations efficiency in the manufacturing industry. The company faced frequent visits to the same customers due to a lack of necessary parts during exchanges. The replenishment of parts by maintenance staff was personalized, making it difficult to track who was taking which parts and how many. To address this, they visualized and analyzed two aspects: maintenance parts inventory and past usage records. The site manager implemented management based on the inventory status held by subordinates and ensured thorough replenishment of maintenance parts, leading to improved operational efficiency for maintenance staff by reducing the number of visits. 【Case Overview】 ■ Industry: Manufacturing ■ Operations: Maintenance ■ Challenges: Basic aggregation, visualization, reduction of man-hours ■ Analytics and AI Solution - Visualization and analysis of maintenance parts inventory and past usage records *For more details, please refer to the PDF document or feel free to contact us.

• Business Intelligence and Data Analysis

Utilizing DataRobot! A case study on reducing response labor during maintenance inspections and abnormal occurrences. We will introduce a case that achieved quality improvement in equipment maintenance in the manufacturing industry. The company had a challenge of wanting to understand whether installed parts were constructed according to recommended installation conditions, and if they differed from those conditions, where the abnormal areas were located. By using DataRobot, a model was developed to infer the abnormal areas based on sensor data regarding the installation status of the parts. This resulted in a reduction of response labor during maintenance inspections and abnormal occurrences. 【Case Overview】 ■ Industry: Manufacturing ■ Business: Equipment Maintenance ■ Challenge: AutoML tools, factor analysis ■ Analytics & AI Solution - Developed a model to infer abnormal areas based on the installation status of parts *For more details, please refer to the PDF document or feel free to contact us.*

• Business Intelligence and Data Analysis

We would like to introduce a case study on the improvement and automation of inspection processes in the manufacturing industry. There was a challenge to reduce the number of functional inspections conducted multiple times on production lots of electronic components, improve throughput, and limit investment in inspection equipment. To address this, we used information from the first inspection to predict the results of the second inspection using AI. Lots that were deemed sufficient after the first inspection were stopped there, thereby improving throughput in functional inspections. 【Case Overview】 ■Industry: Manufacturing ■Business: Quality Inspection ■Challenges: Production Efficiency, Operational Efficiency, Quality Assessment ■Analytics/AI Solution - By stopping inspections for lots deemed sufficient after the first inspection, we improved throughput in functional inspections. *For more details, please refer to the PDF document or feel free to contact us.

• Business Intelligence and Data Analysis

We will introduce examples of improvements and automation in the inspection process within the manufacturing industry. There was a challenge to reduce the frequency of inspection errors by discovering and improving the factors in the manufacturing process that lead to inspection errors for electronic components, as well as to omit or simplify inspection items to enhance production efficiency. To address this, we modeled the relationship between the error occurrence rate for each inspection item and the measured and controlled values of the manufacturing process, identifying and improving the factors that lead to errors, which allowed us to simplify or omit the relevant inspection items. 【Case Overview】 ■Industry: Manufacturing ■Business: Quality Inspection ■Challenges: Improving inspection efficiency, enhancing quality, factor analysis ■Analytics and AI Solutions - Modeling the relationship between error occurrence rates and the measured and controlled values of the manufacturing process - Identifying and improving the factors leading to errors, simplifying or omitting the relevant inspection items *For more details, please refer to the PDF document or feel free to contact us.

• Business Intelligence and Data Analysis

We will introduce a case study of optimizing indoor environments and reducing power consumption using air conditioning control AI in the manufacturing industry. In environments where temperature management is essential, such as food factories, the challenge was to automatically control air conditioning through AI's autonomous learning to achieve an appropriate indoor environment and energy savings. We adopted a policy to train AI that simultaneously achieves room temperature maintenance and power consumption reduction through reinforcement learning in a simulation environment, aiming for the rapid development of control AI that can be transferred to actual equipment. By using a simulation environment, we successfully accelerated the development of control AI to be 1000 times faster compared to reality. 【Case Overview】 ■Industry: Manufacturing ■Business: Operational Optimization ■Challenges: Operational Optimization, Autonomous Control, Cost Reduction ■Analytics and AI Solutions - Adopted a policy to aim for the rapid development of control AI that can be transferred to actual equipment - Conducted AI development focused solely on maintaining room temperature as a Proof of Concept (PoC) *For more details, please refer to the PDF document or feel free to contact us.

• Temperature and Humidity Control

We would like to introduce a case where AI enabled the stabilization of product quality and analysis of variation factors in the manufacturing industry. In the quality inspection tests for drive system products, there was a challenge to analyze the variation factors of the inspection results using the data obtained from the tests, with the aim of improving manufacturing conditions and other aspects. To address this, we developed a model to predict the variation in inspection results using measurement conditions from the quality inspection tests, design information of the manufactured products, and component dimensions as features. By analyzing the parameters of this model, we interpreted them as factors and linked them to improvements in manufacturing conditions. [Case Overview] ■ Industry: Manufacturing ■ Business: Quality Inspection ■ Challenges: Factor Analysis, Improvement of Inspection Efficiency, Production Efficiency ■ Analytics/AI Solution - Developed a model to predict the variation in inspection results *For more details, please refer to the PDF document or feel free to contact us.

• Business Intelligence and Data Analysis

We would like to introduce a case that enabled the prediction of consumable replacements at a copier manufacturer. The timing for replacing consumables such as copier toner varies by customer, so a method of regularly delivering consumables leads to waste in inventory and logistics. Therefore, we developed an AI engine to predict consumable replacements for each customer. This allows for replenishment/replacement before consumables run out, reduces waste in consumable inventory, and further streamlines the logistics of consumables. 【Case Overview】 ■Industry: Manufacturing ■Business: After-sales service ■Challenges: Reducing logistics costs, optimizing inventory ■Analytics/AI Solution ・Developed an AI engine to predict consumable replacements for each customer *For more details, please refer to the PDF document or feel free to contact us.

• Business Intelligence and Data Analysis

We will introduce a case study on the correlation analysis of electricity usage and home appliance specifications. There was a challenge to clarify the correlation between the specifications of home appliances owned by households (such as rated power consumption and manufacturer) and the actual electricity consumption. We used an OCR service to read the specification information listed on the appliances, and based on that information, we constructed and validated a power consumption prediction model and conducted a correlation analysis. In the future, we will work on improving the accuracy of the reading logic for specification information and obtaining more precise information such as household occupancy times. [Case Overview] ■ Industry: Electricity ■ Business: Maintenance Management ■ Challenge: Correlation Analysis ■ Analytics & AI Solutions - Read appliance specification information using OCR services - Construct and validate a power consumption prediction model and conduct correlation analysis based on the information *For more details, please refer to the PDF document or feel free to contact us.

• Analytical Equipment and Devices

We would like to introduce a case that enabled automatic determination of whether a person is at home or not (One to One After Follow). In the power industry’s maintenance management operations, we were considering predicting whether people are at home or not based on the electricity consumption of residences and offices, and linking this to initiatives such as peak shifting and monitoring services. Therefore, we made it possible to determine whether a person is at home or not using machine learning algorithms on the electricity consumption data obtained from smart meters. [Case Overview] ■ Industry: Power ■ Business: Maintenance Management ■ Challenges: Improving operational efficiency, service development ■ Analytics & AI Solution - Determining whether a person is at home or not using machine learning algorithms from electricity consumption data *For more details, please refer to the PDF document or feel free to contact us.

• Business Intelligence and Data Analysis

We will introduce a case study of automatic control of heavy machinery using reinforcement learning-based AI. In heavy machinery operations, there was a challenge of unstable quality among technicians due to a lack of technical inheritance caused by a declining workforce. Additionally, there were significant hurdles in collecting data for heavy machinery operations, necessitating the establishment of a safe method for data collection. To address this, we constructed a simulator that reproduces certain operations of heavy machinery using sensor data installed on the machinery, employing machine learning techniques. We developed a control AI through reinforcement learning within that simulator environment. As a result, we were able to gain insights into the technical possibilities for improving operational efficiency. 【Case Overview】 ■Industry: Construction ■Business: Construction Work ■Challenge: Labor Reduction ■Analytics & AI Solution - Simulator Development We built a simulator that reproduces certain operations of heavy machinery using sensor data installed on the machinery. The behavior of the sensor data in response to control signals for the actual machinery was learned. - Control AI Development The AI was trained to minimize the error between the simulator's operation and target values in response to control signals. *For more details, please refer to the PDF document or feel free to contact us.

• Other process controls

We will introduce a case where fault prediction detection was realized in wind power generation facilities. When a wind turbine experiences a failure, unplanned downtime occurs. By detecting signs of failure, we aimed to improve the efficiency of maintenance and inspections and enhance the operational efficiency of wind turbines. To address this, we developed an AI to detect failure signs from operational sensing data. This resulted in reduced operational costs through more efficient maintenance and inspection work, as well as improved operational rates by preventing unexpected accidents. 【Case Overview】 ■Industry: Social Infrastructure ■Business: Operations and Maintenance ■Challenge: Improvement of Inspection Efficiency ■Analytics and AI Solution - Determine abnormal conditions when deviating from a steady state - Predict whether equipment in an abnormal state will fail subsequently *For more details, please refer to the PDF document or feel free to contact us. - Related link - https://www.tdse.jp/case-study/fault-detection/

• Embedded system design service

We will introduce a case study of improvement in abnormal detection of overhead wires using deep learning. At Tokyo Electric Power Grid, abnormality detection was performed by humans visually inspecting aerial footage as part of overhead wire inspection and equipment maintenance work. The visual confirmation involved slow playback of the aerial video at one-tenth speed, making the inspection process very costly, and there were also issues with accuracy due to missed detections. To address this, a model was developed using deep learning to determine abnormalities and normal conditions from image data. By quantifying and visualizing the "abnormality" of the overhead wires, AI was able to identify areas that required human visual confirmation, resulting in a reduction in the costs associated with visual inspections.

• Image analysis software

Machine learning is a technology that infers and predicts results for unknown data by iteratively learning from data on a computer. It can be said that machine learning is not suitable for situations that require real-time decision-making, such as anomaly detection or responding to environmental changes like the automatic control of heavy machinery. So, what technologies can be utilized in such cases? This document provides a detailed explanation of the advantages of advanced technologies that can be applied in various scenarios within the construction and telecommunications infrastructure industries, along with examples of their use in business, illustrated with diagrams and images. Please take a look in light of your company's challenges and objectives!

• Software (middle, driver, security, etc.)

Image DX that enables the efficiency and cost reduction of visual inspections and assessments, such as inspections of social infrastructure like roads and bridges, structural surveys of buildings, and deterioration diagnosis of utility poles and power lines. This document provides a detailed introduction to the reasons why Image DX is gaining attention, the basic technologies involved, examples of solutions, and points to consider when promoting Image DX. It is a useful resource for those considering the utilization of image data. Please take a moment to read it! 【Contents (partial)】 ■ What is Image DX ■ Reasons for the attention on Image DX ■ Basic technologies in Image DX ■ Examples of Image DX solutions ■ Points to consider when promoting Image DX ■ Initial steps to start Image DX *For more details, please refer to the PDF document or feel free to contact us.

• Image analysis software

This document introduces effective approaches utilizing image DX (Digital Transformation) that are likely to yield results. It provides a detailed explanation of scenes where image DX is applied, trends in image DX, and characteristics of DX/AI solution development. Would you like to create new value in your business by leveraging image data? 【Contents (partial)】 ■ What is Image DX? ■ Scenes of Image DX Application Manufacturing: Improvement of quality control Construction and social infrastructure industry: Abnormal detection for security at sites and facilities Retail: Automated checkout using image analysis Back-office operations: Business efficiency improvement using AI OCR ■ Trends in Image DX ■ Characteristics of DX/AI Solution Development ■ What are AI solutions for priority business challenges? When considering highly feasible solutions, let's examine five perspectives each from business and analytics viewpoints. ● Business Perspective Specificity of measures Psychological factors due to AI substitution Risks Constraints Generality of challenges ● Analytics Perspective Requirement for accuracy and speed Difficulty of data acquisition Data quality Ease of problem-solving *For more details, please refer to the PDF document or feel free to contact us.

• Image analysis software

Image DX, which utilizes image processing technology and machine learning technology, is expected to improve quality control and production efficiency in the manufacturing industry. This document introduces its appeal, the challenges of implementation, and key points to get started. 【Contents (Partial)】 ■ What is Image DX ■ Reasons for the Growing Attention on Image DX ■ Examples of Technologies in Image DX ■ Three Major Solutions of Image DX ■ Challenges in Implementing Image DX ■ Initial Steps to Start Image DX Each item is explained in detail, making it a useful reference when considering implementation. *For more details, please refer to the PDF document or feel free to contact us.

• Image analysis software

Our company has been solving various challenges through an appearance inspection system utilizing AI. We can automate inspections and checks that relied on human eyes, such as equipment maintenance and product quality verification, leading to increased efficiency and cost reduction. TDSE has a proven track record of supporting data utilization across various industries. For example, in the equipment maintenance of a major power company's transmission and distribution network, AI has significantly shortened the inspection time for transmission lines spanning approximately 14,500 kilometers. We have achieved results such as reducing costs that were previously incurred through visual inspections. In recent years, we released the appearance inspection AI system 'TDSE Eye,' which can be built by training it with just a small number of normal images (images of good products). With the AI model built on the cloud, a high-performance anomaly detection AI is always available, allowing for easy system implementation and operation without specialized knowledge. *Various materials such as "Examples of AI-based appearance inspection," "TDSE Eye product documentation," and "Technical explanation materials" can be viewed immediately via PDF download.*

• Visual Inspection Equipment

"TDSE Eye" implements advanced algorithms to streamline maintenance tasks and visual inspections of product quality. An AI model is built using only images of normal conditions. In the cloud, simply uploading images allows for the identification of abnormal images. On edge devices, abnormal image identification is performed by downloading the portable AI server and AI model. We also offer system implementation tailored to your company's environment and various support services, so please feel free to consult us when needed. 【Process until Implementation】 ■STEP 01: Prepare Images (If there are variations in normal conditions, images corresponding to all variations are required) ■STEP 02: Build AI Model ■STEP 03: Image Identification (Cloud and Edge Device Compatible) *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

The anomaly detection service "TDSE Eye" provides access to always new, high-performance AI models built in the cloud. Model construction can be done using only a few normal images, eliminating the need to collect abnormal images. Additionally, by using a portable AI server, the AI models built in the cloud can be utilized on edge devices such as PCs that are disconnected from the network. It is also easy to use AI from individual applications. 【Five Reasons to Choose Us】 ■ Always new, high-performance anomaly detection AI ■ Can be implemented and operated without specialized knowledge ■ Built at low cost and in a short period ■ Offline execution of AI inference at the edge ■ Extensive track record in the field of image AI *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

In visual inspections of tunnel walls by humans, the work is subjective and it is difficult to pass on skills, and there is a risk of deteriorated areas falling. By utilizing the abnormality detection service "TDSE Eye" and conducting inspections with drone images and AI, it is possible to standardize inspection quality and automate and reduce manpower in the work. This product allows for easy operation through a web interface to build AI, and it can be implemented and operated without specialized knowledge. [Overview] ■ BEFORE - Time-consuming and costly in terms of manpower, work is subjective and skill transfer is difficult, with a risk of deteriorated areas falling. ■ AFTER - Automate and reduce manpower in inspection work with AI, standardize inspection quality with AI, and ensure safety with drone photography. *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

In human visual inspections, it is necessary to conduct a full inspection, which incurs time and labor costs, and the work is subjective, leading to inconsistencies in inspection quality. By utilizing the anomaly detection service "TDSE Eye," we can automate the process by selecting only those products that require human inspection through AI inspections, thereby reducing manpower and ensuring consistent inspection quality. Additionally, this product can quickly build complex models in a cloud environment and execute AI inference offline at the edge. 【Overview】 ■BEFORE - Time and labor costs are high - Work is subjective, leading to inconsistencies in inspection quality ■AFTER - Automate the process by selecting only products that require human inspection through AI inspections - Ensure consistent inspection quality with AI *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

In visual inspections, automating the inspection process is difficult due to the structure of iron pipes, resulting in high time and labor costs. By combining the abnormal detection service "TDSE Eye" with a 360-degree camera, we can detect abnormalities such as internal damage from the captured images and identify areas that require visual inspection. This eliminates the need to verify everything visually, achieving a reduction in labor and time costs. Additionally, our product allows for easy operation through a web interface to build AI, enabling implementation and operation without specialized knowledge. 【Overview】 ■BEFORE - Automating the inspection process is difficult due to the structure of iron pipes - High time and labor costs ■AFTER - Efficiently capturing the interior with a 360-degree camera - Reducing labor and time costs by inspecting camera images with TDSE Eye *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

"TDSE Eye" implements advanced algorithms to streamline visual inspections such as equipment maintenance and product quality checks. When building AI with normal and abnormal images, the emergence of various abnormal conditions can prolong the image collection process, resulting in significant time required before implementation and operation can begin. This product allows for AI training using only a small number of normal images, enabling quick and cost-effective AI development. Since there is no need to prepare abnormal images, image collection is simplified. *If there are variations in the normal state, all variations must be trained as normal. [Features] ■ AI built using only normal images ■ Implementation of advanced algorithms ■ Consistently high-performance anomaly detection AI ■ Easy implementation and operation without specialized knowledge ■ Offline execution of AI inference at the edge *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

The appearance inspection system "TDSE Eye" determines abnormalities in various textures and objects. When it detects abnormalities such as chips, cracks, fraying, tears, or breaks, the degree of abnormality in the appearance of the image differs from the normal images used for model creation. It may also be capable of detecting any unusual appearance abnormalities (such as unknown foreign objects). Additionally, this product can be implemented and operated without specialized knowledge. It can build complex models in a short period within a cloud environment. 【Examples of Abnormality Detection (Partial)】 < Texture Types > ■ Carpet ■ Grid ■ Leather and leather products ■ Tile ■ Wood and more < Object Types > ■ Bottle ■ Cable ■ Capsule ■ Food ■ Wood ■ Tablet ■ Screw ■ Brush ■ Electronic components ■ Zipper and more * For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

"TDSE Eye" is an anomaly detection service that streamlines visual inspection tasks, such as equipment maintenance and product quality verification, using AI for appearance inspections. The specific areas of the identified images that are abnormal are quantified and can be visualized as a heatmap. Additionally, it is possible to run the AI model built in the cloud on edge devices, such as PCs that are disconnected from the network. 【Features】 ■ AI model construction without programming (Web interface) ■ Continuously provides cutting-edge beneficial image AI technology in a cloud environment - Anomaly detection AI is built solely on normal images - Allows for visual confirmation of abnormal areas ■ AI model can also be utilized on edge devices like PCs that are disconnected from the network *For more details, please refer to the PDF document or feel free to contact us.

• Visual Inspection Equipment

"Veteran workers are relied upon, and inspections are becoming person-dependent." "Visual inspections take time and human costs." "We cannot introduce AI without specialized knowledge." All these concerns will be resolved by 'TDSE Eye.' AI will take over the visual inspections that have relied on humans until now, enabling efficiency and cost reduction in operations. It can be implemented and operated without specialized knowledge, and it can be built at low cost and in a short period. 【Features of TDSE Eye】 ■ Implementation of advanced algorithms ■ Streamlining visual inspections for maintenance tasks and product quality checks ■ By building AI models in the cloud, high-performance options are always available ■ Easy operation through a web interface to build AI ■ Offline execution of AI inference at the edge ■ Extensive track record in the field of image AI *For more details, please refer to the related links or feel free to contact us.

• Visual Inspection Equipment

Regular inspections of concrete deterioration (such as cracks) in tunnels and bridges conducted by human eyes are very time-consuming and can sometimes be dangerous. Therefore, we use images captured by drones to automatically detect cracks and other issues with AI, making the inspection process safer and more efficient. Our anomaly detection service, 'TDSE Eye,' allows for the rapid construction of complex models in a cloud environment, enabling implementation and operation without specialized knowledge. [Case Overview] ■ Challenges - Time and labor costs are high, the work is dependent on individuals, making skill transfer difficult, and there is a risk of falling debris from deteriorated areas. ■ Benefits - Automation and reduction of inspection work through AI, maintaining consistent inspection quality with AI, and ensuring safety through drone imaging. *For more details, please refer to the PDF document or feel free to contact us.

• Visual Inspection Equipment

Visual inspection of defective products on production lines by humans involves a high workload and leads to inconsistencies in inspection quality. Therefore, we first use AI to identify products with a high likelihood of defects. Only those selected products are then visually re-inspected by humans, ensuring consistent quality while reducing manpower. Our anomaly detection service, 'TDSE Eye', implements advanced algorithms to streamline visual inspections for maintenance tasks and product quality verification. 【Case Overview】 ■Challenges - Time and labor costs are high - The work is dependent on individuals, leading to inconsistencies in inspection quality ■Benefits - AI inspection selects only the products that require human inspection, reducing manpower - AI ensures consistent inspection quality *For more details, please refer to the PDF document or feel free to contact us.

• Visual Inspection Equipment

The use of AI requires specialized knowledge and tools, and in reality, it is limited to a few companies that have secured personnel knowledgeable in AI and IT. Our company has built an image recognition AI modeling platform called "TDSE Eye" and has started providing various products necessary for real business. AI models can be built without programming (Web interface), and AI will streamline visual inspections, such as maintenance tasks and product quality checks. **Features of TDSE Eye** - AI model building without programming (Web interface) - Continuously provides cutting-edge beneficial image AI technology in a cloud environment - Anomaly detection AI is built using only normal images - It is possible to visually confirm abnormal areas - Portable AI server enables anomaly degree calculation on edge devices like PCs *For more details, please refer to the PDF document or feel free to contact us.*

• Visual Inspection Equipment

"TDSE Eye" implements advanced algorithms to streamline visual inspections such as equipment maintenance and product quality checks. By building AI models in the cloud, high-performance solutions are always available. AI can be constructed easily through a web interface. Our company has handled numerous projects in the field of image AI, and this service reflects that experience. 【Features】 ■ Always high-performance anomaly detection AI ■ Can be introduced and operated without specialized knowledge ■ Built at low cost and in a short period ■ AI inference can be executed offline at the edge ■ Extensive track record in the field of image AI *For more details, please refer to the PDF document or feel free to contact us.

• Visual Inspection Equipment

"Cognigy" is a platform that allows you to design and develop highly scalable conversational AI in a short period of time, thanks to its excellent editor features. It can be intuitively configured through a GUI and supports numerous external integration connectors as standard. You can flexibly design and operate interactive systems that engage in natural conversations. Please feel free to contact us if you have any inquiries. 【Features】 ■ All-in-one for AI development, operation, and analysis ■ Intuitive AI development with low code ■ Support for over 20 languages *For more details, please download the PDF or contact us.

• Software (middle, driver, security, etc.)