In response to customer demand on a wide variety of products in small quantities in the manufacturing industry, there is an urgent need to find AI solutions from the cloud to terminals Claireye Intelligence provides a solution that integrates software and hardware - BailAI image inspection solution to assist traditional manufacturing industries in improving process efficiency and product quality, thereby achieving the initial goal of transformation After the government declared 2017 to be Taiwan's "First Year of AI," AI startups have sprung up in Taiwan Established in 2018, Claireye Intelligence targets smart manufacturing and provides a platform for AI image analysis and process optimization, using the power of deep learning to detect product defects and abnormalities in the assembly process It assists companies in building infrastructure from terminals to the cloud, which enables automated monitoring of factory production to improve process efficiency and quality Focusing on AI image inspection based on its familiarity with the production line quality control process Shirley Liu, founder and CEO of Claireye Intelligence, is a young entrepreneur She entered the manufacturing industry after graduating from college and held a quality control position in the plastic injection process of hard disk parts "She was already on the production line at the time, and is familiar with the production line process of production machinery" She later switched career paths to marketing and planning, and then worked as an AI product manager When the time came, Shirley Liu decided to start a business, focusing on AI image recognition in the manufacturing industry "The difficulty for enterprises is the lack of an AI development team Even if an enterprise has an AI team, development projects will take a lot of time, at least 6-12 months" said Shirley Liu, who is well versed in the market's pain points The problem that needs to be solved by platforms is to provide services that allow traditional manufacturing industries to build their own AI models without needing employees with a programming background, and to remotely assist production lines with troubleshooting and subsequent system maintenance, helping companies save development time and labor costs BailAI image inspection platform usage scenarios Facing the large number of competitors that provide AI image recognition in the market, what are the technical advantages of Claireye Intelligence Shirley Liu said that many companies currently have AOI equipment, but the bottleneck in the application of AOI is that it can only be used for defect inspection in fast production of large quantities, and parameters need to be adjusted after each inspection or production Based on her understanding of the industry, most SMEs are limited by their financial resources due to AOI equipment often costing over NT1 million, but they also want to use automated inspection This is where Claireye Intelligence comes in Shirley Liu went on to say that it is impossible for traditional manufacturing industries to maintain a technical team that includes AI engineers, data engineers, cloud architects, and terminal architects Claireye Intelligence specializes in software and hardware integration Enterprises can use the BailAI image inspection platform to easily solve inspection problems on the production line In other words, customers only need to provide images or samples for Claireye Intelligence to carry out model training, model deployment, and system integration, and they can easily use AI technology to optimize and monitor production line processes Participated in the AI New Talent Selection and achieved a recognition rate of over 90 in assembly behavioral image recognition For example, a certain connector manufacturer only has 1-2 AI engineers in its technical team The main problem that needs to be solved is that most operators are on the production line, while quality control and senior managers are not on site, and the company wants to understand the actual situation of the production line through remote monitoring Claireye Intelligence uses industrial cameras to capture production line images, and transmits AI image analysis to the remote end Supervisors and quality control personnel can observe if there are any errors in the production line assembly, such as whether the connectors and lines are connected properly, through the monitor Claireye Intelligence's AI image inspection operates on Microsoft's Azure cloud platform, and also utilizes terminal equipment, such as NVIDIA's edge computing equipment placed around the inspection station, to assist traditional manufacturing industries with improving production line efficiency and detecting problems early through an integrated solution from the cloud to terminals Claireye Intelligencersquos customers currently include aviation, electronic peripherals, connectors, and metal industries Assembly process solution for human behavior recognition in assembly lines achieves an accuracy of over 90 In order to demonstrate the depth of technology, Claireye Intelligence participated in the 2021 AI New Talents Selection of the Industrial Development Bureau, Ministry of Economic Affairs, and provided Lite-On Technology with the "assembly process solution for human behavior recognition in assembly lines" The solution determines effective working hours and ineffective working hours of operators on the production line through cameras and AI image recognition It recognizes hand posture and position through images to determine the operator's assembly behavior, achieving an accuracy of over 90 Shirley Liu added that the assembly process of electronic components is complex, mostly carried out manually, and cannot be replaced by robotic arms Claireye Intelligence used cameras to film the assembly process of operators at Lite-On's assembly station The algorithm is then trained and corrected based on the video, and the final trained model can directly determine whether there are any errors in the assembly process to improve the overall process Project development time is expected to be shortened to 1 month by using the BailAI image inspection platform Since its establishment more than three years ago, Claireye Intelligence has accumulated a considerable amount of project experience and hopes to commercialize the project experience Shirley Liu pointed out that the trial version of BailAI image inspection will be completed this year 2022 Customers can choose industrial cameras or video cameras based on the detail of the object being inspected It can even use X-rays to capture images, and then the images are automatically marked by the platform Claireye Intelligence will provide customers with AI application models suitable for the field Inferences can also be made in the cloud or terminals for launch in the manufacturing industry The metals industry, metal casings of industrial computers, connectors, electronic peripherals, and mechanical parts can all use the platform for defect detection and object identification Claireye Intelligence will continue to improve its technical capabilities, accumulate customer experience to complete commercialization, and also accelerate the implementation of AI inspection applications In the mid-term, it will build terminal and cloud infrastructure and shorten the development time of enterprise AI projects from 6-12 months to 1 month, reducing usage time and lowering the threshold for enterprises The long-term goal is to target the Southeast Asian market where Taiwanese businesses are gathered, expand software and hardware integrated AI solutions to overseas markets, and expand the scale of operations

Artificial intelligence AI has gradually changed the way various industries operate in recent years However, most of the work is still done by humans, with AI playing a supporting role This has led to emergence of the term "AI Copilot," which stands for "AI-driven tools or assistants" that aim to assist users in completing various tasks and improve productivity and efficiency The concept of AI Copilot comes from the role of "co-pilot" During flight, the co-pilot assists the main pilot in completing various tasks to ensure flight safety and efficiency In fact, there have been signs of various "machines" beginning to play the role of "copilot" in different fields since the Industrial Revolution, assisting humans in completing heavy physical and repetitive tasks, greatly improving factory production efficiency, and driving rapid economic development Following the advancement of computing equipment and breakthroughs in machine learning, deep learning, and image recognition technologies, the concept of AI Copilot has gradually taken shape The development of AI Copilot marks the transition from "machine-assisted to AI-assisted" Early robots could only complete preset repetitive tasks, but today's AI copilot can learn and adapt to new environments and tasks, and continuously optimize its performance in practical applications This transformation not only changes human-machine interactions, but also has a profound impact on various industries The application scope of AI copilot covers various industries, including finance, healthcare, manufacturing, education, retail, etc, and are everywhere to be seen Application of AI copilot in the retail industry AI image recognition checkout In the retail industry, the application of AI copilot has begun to show concrete results Take Viscovery's AI image recognition checkout system as an example This system is a type of AI copilot model that helps store clerks speed up checkout or assists consumers in simplifying the self-service checkout process The store clerk needs to scan the product barcodes one by one in the regular checkout method If a product does not have a barcode, such as bread and meals, the clerk needs to first visually confirm the items, and then input them into the POS checkout system one by one Based on actual measurements at a chain bakery, it takes 22 seconds for an experienced clerk from "visual recognition" to "entering product information of a plate of 6 items into the checkout system" New clerks may need even more time In addition, according to a Japanese bakery operator, it takes 1 to 2 months to train employees to become familiar with products Now with AI image recognition technology, store clerks let AI handle the "product recognition" step, and AI will play the role of copilot, quickly identifying items within 1 second, speeding up checkout to save 50 of checkout time, and optimizing customers'shopping experience The time cost of training employees to identify bread can also be effectively shortened Even for products with barcodes, AI can quickly identify multiple items in one second, which is more efficient than scanning barcodes one by one The self-checkout system "assisted" by AI image recognition allows consumers to successfully complete shopping without the help of store clerks, eliminating the trouble of swiping barcodes or searching for items on the screen, which improves the shopping experience In a time when store clerks are hard to hire due to labor shortage, this also helps stores reduce operating costs AI quickly identifies multiple checkout items in just one second Source of image Viscovery Recently, startups dedicated to developing AI image recognition checkout solutions have emerged in various countries The most lightweight solution currently known is in Taiwan It can be immediately used by installing a Viscovery lens and a tablet installed with Viscovery AI image recognition software at the checkout counter to connect to the store's existing POS checkout system There are various integration methods, including plug-and-play and API solutions integrated with the store's POS system Viscovery AI image recognition system can be painlessly integrated with the store's existing POS system Source of image Viscovery Example of AI image recognition checkout Currently, the Viscovery AI image recognition system is being used in bakery chains in Taiwan, Chinese noodle shops in Singapore, micromarkets in department stores in Sendai, Japan, and Japanese bakeries and cake shops Over 7 million transactions were completed through this AI system, which identified more than 40 million items These use cases demonstrate the extensive application of the Viscovery AI image recognition system in the retail industry In the future, the company will continue to explore the various possibilities of using Vision AI in retail and catering nbsp The Viscovery AI image recognition system is already being used in bakeries, cake shops, restaurants, and convenience stores in Japan, Singapore, and Taiwan Source of image Viscovery

Having experienced small to medium-sized e-commerce, founders and CEO Zi-Hao Huang and co-founder and COO Yi-Ting Li of Tammy Technology decided to tackle the marketing challenges of all e-commerce personnel using AI technology Focusing on fashion e-commerce, Tammy Technology's personalized recommendation SaaS Software as a Service helps small to medium enterprises tackle the rising costs of marketing and the overwhelming data, enhancing conversion rates and average order value, becoming an invaluable AI assistant in the fashion e-commerce industry Enhancing conversion rates has always been a major challenge for all e-commerce platforms Unlike major players like Google and Facebook who collect browsing history to target interested messages or advertisements, smaller e-commerce businesses lack the resources and manpower to construct data analysis systems or tools Company Position Marketing technology team for small and medium-sized e-commerce utilizing AI to establish automated marketing Established in 2016, Tammy Technology initially aimed to become 'the marketing technology team for small to medium-sized e-commerce,' hoping to resolve challenges of low conversion rates and customer retention through data analysis and personalized recommendation services 'Daniel referring to Zi-Hao Huang and I have both worked in small to medium e-commerce I handled branding, marketing, and design, while he managed backend systems, project management, and development launches, almost everyone was multitasking' Understanding the pain points of e-commerce operators, they decided to help small and medium enterprises by developing automated marketing systems and a personalized recommendation SaaS, aiding those with limited marketing resources Through AI technology, analyze consumer purchase preferences and provide personalized recommendation services By analyzing each consumer's purchase preferences using AI technology and storing their digital footprints in the backend for data analysis, Tammy Technology stands apart from traditional recommendation systems that categorize customers They offer personalized product recommendations based on each individual's style preferences to achieve precise marketing objectives Yi-Ting Li emphasizes that Tammy Technology's personalized recommendation service has two main functions one is to make website personalized recommendations, and the second is to integrate with marketing channels such as email, SMS, and chatbots chatbot to send personalized promotional messages When consumers enter the official website, based on consumer profiles and preferences, different product recommendations can be provided on each page Individual products also have different recommendation systems on different website pages, providing each consumer with a unique shopping experience after entering the site Using deep learning AI technology, Tammy Technology analyzes consumer behaviors at various online shopping touchpoints through various devices, digitalizing all consumer behavioral data to build consumer profiles For example, a consumer with high purchasing frequency designated as VIP, whose preferences for Morandi color schemes, lace materials, high-neck styles, etc, are recorded and tagged When browsing the store's website, the website will recommend corresponding products based on these purchase preference tags for VIP, and can also set customized discounts and pop-up windows, or send personalized messages through mobile SMS, email, etc, ensuring each customer receives discounts that fit their needs At this stage, Tammy Technology's client base includes male and female apparel, shoes, accessories, cosmetics, and other fashion design industries with a strong personalized color Many clients have seen significant results after introducing personalized recommendation services For example, Shu Uemura under the L'Oreal Paris group, specializing in high-performance skin products and trendy makeup products and professional makeup tools, experienced a significant increase in conversion rates by 3 times and a revenue increase of 18 times after adopting Tammy Technology's service Additionally, the well-known apparel business iRoo, after installing a personalized recommendation system on the official website and integrating digital marketing channels like Line and Chatbot, saw the conversion rate rise more than 5 times, with a monthly revenue increase of 21 The pricing model is subscription-based, with large corporations being charged based on specific feature needs The overall effect after customer usage achieves at least a 3 times increase in conversion rate, and an average of 2 times growth in average order value New client base After apparel and cosmetics, pushing into the home decor industry Despite the overall increase in e-commerce sales, the personalized recommendation service has shown impressive results However, due to the pandemic, non-essential fashion industries have been impacted Tammy Technology, which primarily relies on the fashion design industry, saw a decrease in e-commerce performance by about 30 during the pandemic To diversify operational risks, starting from 2022, Tammy Technology expanded its service clientele to include distinctive, quality-type home furniture and living products As for why not expand the client base to 3C products Yi-Ting Li explains, 3C products focus on cost-effectiveness and brand strength, like Apple computers which have a loyal group of fans, making it challenging to sway their purchasing behavior However, apparel, cosmetics, and home decor focus on personalization, style, and taste, like apparel with the fast, short-term 'fast fashion' trends, updating products weekly, and seasonal characteristics, updating personalized recommendations every 3-5 days and cosmetics, including makeup and skincare, which have high customer loyalty and repurchase rates, are most suitable for recommendations and proactive discount messaging Makeup and outfits represent personal taste, thus these two industries can complement each other in personalized recommendations To accelerate the process of integrating businesses with personalized recommendations, it is necessary to establish an SOP,' Yi-Ting Li continued, 'Tammy Technology, by introducing recommendation services in the fashion design industry, has constructed a service process SOP to facilitate rapid successful experience replication, expecting to quickly integrate into the life and home industry in the second half of the year New business model Establishing a new marketing model centered on the consumer Currently, Tammy Technology's client number has exceeded 2000, including globally renowned fashion brands like L'Oreal and BLUE WAY The future focus will remain overseas, aiming for markets like Hong Kong, Singapore, and North America Tammy Technology, with its successful experience of integrating recommendation systems into fashion e-commerce, has gained investors' favor, securing investments from domestic and international accelerators in 2021, raising a total of 70 million New Taiwan dollars, allowing for workforce and scale expansion Future plans include integrating investor resources to establish a new marketing model centered on consumers Tammy Technology co-founder and COO Yi-Ting Li「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」