Stories of companies that succeeded in transforming AI with Letsur

To develop new bio products, InvivoTech needed to reference a vast number of research papers—over 10,000 in total—and manually extract more than 20 data points from each. This process took more than 30 minutes per paper, creating significant inefficiency in research operations and delaying product development.

Letsur developed an NLP-based AI model that automatically analyzes paper sections and extracts key information sentences. By summarizing and visualizing critical data points, the model reduced researchers’ manual workload and dramatically accelerated AI-driven information discovery.

Researchers can now instantly extract only the relevant information from thousands of papers, reducing research time by 98%. Integrated visualization tools also allow easy access to insights, improving overall efficiency across the product planning and development process.

Green is a smart farm operating company that grows high-value crops, and it takes a lot of manpower and time to check growth conditions from time to time and adjust the nutrient supply environment. In particular, work efficiency was declining due to a structure where workers had to directly inspect large farms.

We have developed a computer vision AI that automatically analyzes growth conditions based on crop image data taken with various camera settings. The camera system equipped with AI enabled real-time monitoring, and improved management efficiency by building an automated system for nutrient supply environments and harvesting season notifications.

Everything from crop growth status monitoring to nutrient supply control has been automated, reducing the burden of on-site inspection on workers. Real-time data-based decision-making has become possible, and the accuracy of predicting harvest time has been improved.

Before the introduction of AI, thorough training was considered due to a lack of practical understanding of how to collect, refine, and manage vast amounts of data held by organizations and how it can be used for AI.

It provides practice-oriented training including understanding generative AI technology, learning data collection and quality control systems, and vector DB and LLM-based project examples. It is designed so that the training can understand the overall flow of data planning and AI model development.

We have secured basic knowledge and practical expertise for planning AI projects and proposed data management systems within the organization, and have laid the foundation for planning internal capabilities.

In a situation where new executives had to lead the AI business, it was difficult to plan a strategy in a situation where they lacked background knowledge about AI, and leadership-oriented training was needed to establish a clear direction.

We have organized a practice-oriented curriculum based on intuitive tools such as ChatGPT, Perplexity, and Intuitive AI, and designed so that executives can perform tasks using AI on their own, from planning prompts to establishing strategies and automating reports.

Executives were able to strengthen strategic thinking by directly utilizing AI in practice, and clarified the understanding and execution direction of AX projects within the organization through practice-based training.

In the process of deriving challenges for AI adoption, it was difficult to establish an actionable strategy because it was difficult to collaborate with various stakeholders within the organization and determine the feasibility of technology.

A design thinking-based workshop was conducted with Able Campus to structure the problems of each department and derive issues considering the applicability of AI. Expert engineers participated to review the technology and provide feedback on feasibility.

By redefining the overall business process, each group derived AI challenges that considered both feasibility and business value, and the trainees' understanding of collaboration and the perspective of applying AI in practice increased.

We wanted to introduce generative AI technology into QA work, but there was a lack of practice-oriented training to understand the super-large image generation model and internalize how to apply it to actual work.

Together with the Able campus, we designed a customized AI education curriculum tailored to the characteristics of the QA department, and also provided a Colab-based practice environment that non-experts can follow, and even practice understanding and performance evaluation of the LVM model.

By practicing the entire process from the principles to use of image generation AI, I developed a sense of technology that can be applied to actual QA work. The overall level of satisfaction among trainees was high, and their practical ability to select and evaluate AI models was strengthened.