Stories of companies that succeeded in transforming AI with Letsur

It was necessary to establish daily and monthly sales strategies based on SAP ERP data, but since the ERP system was limited to query-oriented functions, the data had to be downloaded and manually processed for necessary analysis.

We have developed a chatbot based on sales data linked to the SAP ERP system. Users can now perform tasks by automatically generating SQL when querying in natural language, querying the DB, and providing processed results.

Reduced manual analysis time of sales data and reduced repetitive tasks. Real-time data accessibility and work efficiency for establishing sales strategies have been improved together.

SpaceWavy provides a service for designing houses by combining modules according to the purpose desired by consumers. Customer-specific consulting and design processes were essential, and it took an average of about 40 days to provide an estimated bird's-eye view, making quick decisions difficult.

When consumers enter their lifestyle in text, we have developed an AI that recommends SpaceWave's modular product combinations based on the content and provides a bird's-eye view of the estimated house as an image. Using the API integration function provided by StayX, we supported customers to check the modeling results directly on the website.

Customers can check the estimated home design in real time, improving the purchase conversion rate and significantly reducing the time required to make decisions. The existing manual consulting-design process was automated with AI to effectively reduce internal resources.

The company spent a lot of manpower and time during the contract review process. In particular, AI adoption and transformation were difficult due to high security requirements that prevented the use of SaaS-based solutions.

We have developed and implemented an on-premise OCR AI solution to meet security requirements. Accuracy and efficiency were improved through customized learning and error prevention features for each contract form.

By automating the contract review process, manual labor time was reduced and personnel burden was reduced. Security requirements were met by processing on an internal server.

It was necessary to continuously improve the performance of AI CCTV and introduce a systematic system that can be updated and maintained periodically.

Letser provided a solution that automates AI model training, improvement, and maintenance. S-1 has established a meaningful data selection and iterative improvement process based on active learning.

A stable security operating system was established by improving AI model accuracy and reducing maintenance resources. Real-time responsiveness and user satisfaction have been improved.

In rehabilitation medicine, quantitatively measuring patient severity required a complex process that required medical professionals to induce and evaluate patient behavior in a specific environment. We wanted to introduce a quantification system based on images and numbers, but development using complex medical data was difficult due to lack of AI specialists.

We have developed an AI model that can analyze key key points and predict severity indicators based on patient image data. We worked together from planning to collection, pre-processing, model design, and advancement of learning data, and continued to improve prediction accuracy by reflecting feedback from medical staff.

Patient image-based severity prediction AI assists medical staff in evaluating, reducing repetitive workload for medical professionals and providing a more efficient diagnosis and prognosis analysis environment.

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.