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 implemented a production planning optimization model. We formulated a mathematical optimization problem based on constraints such as 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 & 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/

• Scientific Calculation and Simulation Software

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 details (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.

• Database

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 related to environmental regulations, which are updated daily, using natural language processing. To address this, they trained the AI on classification examples from past expert meetings, removing documents from the updates that could clearly be deemed unnecessary. 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. 【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, removing documents from the updates that could clearly be deemed unnecessary. *For more details, please refer to the PDF document or feel free to contact us.

• Document Management System

We will introduce a case study on improving the efficiency of maintenance operations in the manufacturing industry. The company frequently encountered situations where necessary parts were unavailable during exchanges, leading to multiple visits to the same customer. The replenishment of parts by maintenance staff was personalized, making it difficult to track who was taking out which parts and how many. To address this, they visualized and analyzed two aspects: the inventory of maintenance parts and past usage records. The site manager implemented management based on the inventory status held by subordinates, and by thoroughly managing the replenishment of maintenance parts, they improved the efficiency of 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 Solutions - 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.

• Analysis and prediction system

Utilizing DataRobot! A case study on reducing response labor during maintenance inspections and when abnormalities occur. We present a case where quality improvement in equipment maintenance was achieved in the manufacturing industry. The company faced the challenge of wanting to know 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 in response labor during maintenance inspections and when abnormalities occurred. 【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.*

• Analysis and prediction system

We will 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 minimize 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, throughput in functional inspections was improved. *For more details, please refer to the PDF document or feel free to contact us.

• Other analytical equipment

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 simplify or omit 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 leading to errors, which allowed us to simplify or omit the relevant inspection items. [Case Overview] ■ Industry: Manufacturing ■ Business: Quality Inspection ■ Challenge: 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.

• Analysis and prediction system

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 autonomous learning by AI to achieve an appropriate indoor environment and energy savings. A policy was adopted to develop control AI in a short period by training an AI that simultaneously achieves room temperature maintenance and power consumption reduction through reinforcement learning in a simulation environment. By using a simulation environment, we successfully developed control AI 1000 times faster compared to real-world conditions. 【Case Overview】 ■Industry: Manufacturing ■Business: Operational Optimization ■Challenges: Operational Optimization, Autonomous Control, Cost Reduction ■Analytics and AI Solutions - Adopted a policy to develop control AI in a short period by transferring the trained AI to actual equipment. - Conducted AI development focusing 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.

• Automatic Control Equipment

We would like to introduce a case study in the manufacturing industry that enabled the stabilization of product quality and analysis of variability factors through AI. In the quality inspection tests for drive system products, there was a challenge to analyze the variability factors of 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 variability of inspection results using measurement conditions of the quality inspection tests, design information of the manufactured products, and dimensions of components as features. By analyzing the parameters of this model, we interpreted the 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 variability of inspection results *For more details, please refer to the PDF document or feel free to contact us.

• Analysis and prediction system

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.

• Analysis and prediction system

We will introduce a case study on the correlation analysis between 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 OCR services 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.

• Analysis and prediction system

We would like to introduce a case that enabled automatic determination of whether someone is at home or not (One to One After Follow). In the power industry’s maintenance management operations, we aimed to predict whether individuals are at home or not based on the power consumption of residential and office spaces, and connect this to initiatives such as peak shifting and monitoring services. To achieve this, we utilized machine learning algorithms to determine whether individuals are at home or not from the power consumption data obtained from smart meters. 【Case Overview】 ■Industry: Power ■Operation: Maintenance Management ■Challenges: Improving operational efficiency, service development ■Analytics and AI Solution - Determining whether individuals are at home or not using machine learning algorithms from power consumption data *For more details, please refer to the PDF document or feel free to contact us.

• Analysis and prediction system

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 accompanying the decrease in the working population. Additionally, there were significant hurdles in collecting data in heavy machinery operations, necessitating the establishment of a safe method for data collection. Therefore, we constructed a simulator that reproduces part of the heavy machinery's operations 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 and AI Solution - Simulator Development We built a simulator that reproduces part of the heavy machinery's operations using sensor data installed on the machinery. It learns the behavior of sensor data in response to control signals for the actual machine. - Control AI Development The AI learns 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.

• Monitoring and Control Equipment

We would like to introduce a case where the detection of failure precursors in wind power generation facilities has been realized. When a wind turbine experiences a failure, unplanned downtime occurs. By detecting the precursors to failures, we aimed to improve the efficiency of maintenance and inspections and enhance the operational efficiency of wind turbines. To address this, we developed AI to detect failure signs from operational status sensing data. This resulted in reduced operational costs through improved efficiency of maintenance and inspection tasks, as well as increased operational rates by preventing unexpected accidents. 【Case Overview】 ■Industry: Social Infrastructure ■Business: Operations and Maintenance ■Challenge: Improvement of Inspection Efficiency ■Analytics and AI Solution - Determination of abnormal conditions when deviating from a steady state - Prediction of whether the 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/

• Other analytical equipment

We will introduce a case study of improvement in abnormal detection of overhead wires using deep learning. At Tokyo Electric Power Grid, the inspection and maintenance of overhead wires involved humans visually identifying abnormalities from aerial footage. The visual inspection process required the aerial video to be played back at one-tenth speed for manual checking, which was very costly and also had issues with accuracy due to potential oversights leading 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, thereby achieving a reduction in the costs associated with visual inspections.

• Other Software

Machine learning is a technology that infers and predicts results for unknown data by iteratively learning from data using computers. 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 scenes 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!

• Other Software

Image DX enables the efficiency and cost reduction of inspections and assessments through visual means, such as inspections of social infrastructure like roads and bridges, structural investigations of buildings, and deterioration diagnostics of utility poles and power lines. This document provides a detailed introduction to the reasons why Image DX is gaining attention, the fundamental technologies involved, examples of solutions, and important considerations for promoting Image DX. It serves as a useful reference for those considering the utilization of image data. We encourage you to read it! 【Contents (partial)】 ■ What is Image DX ■ Reasons for the attention on Image DX ■ Fundamental technologies in Image DX ■ Examples of Image DX solutions ■ Important considerations for promoting Image DX ■ Initial steps to start with Image DX *For more details, please refer to the PDF document or feel free to contact us.

• Other analytical equipment

This document introduces effective approaches that leverage image DX. It provides detailed explanations of image DX application scenes, trends in image DX, and characteristics of DX/AI solution development. Would you like to create new value in your business by utilizing image data? 【Contents (partial)】 ■ What is Image DX? ■ Image DX Application Scenes 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 Demand 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.

• Other analytical equipment

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 interest in Image DX ■ Examples of technologies in Image DX ■ Three major solutions of Image DX ■ Challenges in implementing Image DX ■ Initial steps to start with 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.

• Other analytical equipment

Our company has been solving various challenges through an appearance inspection system utilizing AI. We can automate inspections and checks that have relied on the human eye, 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 maintenance of transmission and distribution networks for a major power company, AI has significantly reduced 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, 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.*

• Analysis and prediction system

"TDSE Eye" implements advanced algorithms to streamline maintenance operations of equipment 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.

• Measurement and Inspection

The anomaly detection service "TDSE Eye" provides access to always new, high-performance AI models by building them 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.

• Measurement and Inspection

In visual inspections of tunnel walls by humans, the work is subjective, making it difficult to transfer skills, and there is a risk of deterioration falling. By utilizing the abnormal detection service "TDSE Eye" and conducting inspections with drone images and AI, it is possible to standardize inspection quality and automate and reduce labor in the process. 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 and labor costs are high, work is subjective and skill transfer is difficult, and there is a risk of deterioration falling. ■AFTER - Automating and reducing labor in inspection work with AI, standardizing inspection quality with AI, and ensuring safety with drone photography. *For more details, please refer to the related links or feel free to contact us.

• Measurement and Inspection

In human visual inspections, it is necessary to conduct a complete inspection, which incurs time and labor costs, and the work is subjective, leading to inconsistencies in inspection quality. By utilizing the abnormal detection service "TDSE Eye," we can automate the process by selecting only those products that require human inspection through AI inspections, thereby reducing labor. This allows us to maintain a consistent level of inspection quality. Additionally, this product can build complex models in a cloud environment in a short time and can 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 by selecting only products that require human inspection through AI inspections - Maintain consistent inspection quality with AI *For more details, please refer to the related links or feel free to contact us.

• Measurement and Inspection

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, making it possible to implement and operate 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.

• Measurement and Inspection

The "TDSE Eye" is equipped with advanced algorithms that streamline visual inspections for equipment maintenance and product quality verification through visual tasks. When building AI with normal and abnormal images, the emergence of various abnormal states can prolong the image collection process, resulting in significant time required before implementation and operation can begin. This product trains AI using only a small number of normal images, allowing for quick and low-cost AI development. Since there is no need to prepare abnormal images, image collection is simplified. *If there are variations in the normal state, it is necessary to train all variations as normal. 【Features】 ■ AI built using only normal images ■ Implementation of advanced algorithms ■ Consistently high-performance anomaly detection AI ■ Can be implemented and operated without specialized knowledge ■ AI inference can be executed offline at the edge *For more details, please refer to the related links or feel free to contact us.

• Measurement and Inspection

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 to create the model. There is also a possibility of detecting any unusual abnormalities with rare appearances (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 in a cloud environment. 【Examples of Abnormal 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.

• Measurement and Inspection

"TDSE Eye" is an anomaly detection service that streamlines visual inspection tasks, such as equipment maintenance and product quality verification, using AI. 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 like 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.

• Measurement and Inspection

"Veteran workers are relied upon, and inspections are personalized," "Visual inspections take time and human costs," "Unable to introduce AI due to lack of specialized knowledge," and other concerns will all 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 equipment maintenance and product quality verification ■ By building AI models in the cloud, high-performance options are always available ■ Easy operation to build AI through a web interface ■ 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.

• Measurement and Inspection

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 technical knowledge 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.

• Measurement and Inspection

Visual inspection of defective products on production lines by humans involves a high workload and results in inconsistent 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.

• Measurement and Inspection

The use of AI requires specialized knowledge and tools, and in reality, it is limited to companies that have secured personnel knowledgeable in AI and IT. Our company has developed a modeling platform for image recognition AI called "TDSE Eye" and has begun offering various products necessary for real business. AI models can be built without programming (via a web interface), and AI will streamline visual inspections, such as maintenance work on equipment and quality checks of products. **Features of TDSE Eye:** - AI model construction without programming (web interface) - Continuously provides cutting-edge and beneficial image AI technology in a cloud environment - Anomaly detection AI is built using only normal images - It is possible to visually confirm the locations of anomalies - A portable AI server allows for anomaly degree calculation on edge devices like PCs *For more details, please refer to the PDF document or feel free to contact us.*

• Measurement and Inspection

The "TDSE Eye" is equipped with advanced algorithms that streamline visual inspections for equipment maintenance and product quality verification. By building AI models in the cloud, high-performance solutions are always available. AI can be constructed with simple operations through a web interface. Our company has handled numerous projects in the field of image AI, and this service reflects those experiences. 【Features】 ■ Always high-performance anomaly detection AI ■ Can be implemented 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 materials or feel free to contact us.

• Other security systems

"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 via a GUI and supports numerous external integration connectors as standard. You can flexibly design and operate a conversational system that engages in natural dialogue. 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.

• Other Software