On 24 October, at the invitation of the Korea Industrial Intelligence Association (KOIIA), CLEVI CEO Lee Hwan-ho participated in the '2025 Key Talent Development Academy for Mid-Sized Enterprises, Future Industry Response Strategy Seminar' and delivered the keynote speech.
We participated in the 'AI Technology Adoption Cases and Practical DX Strategies' session with a presentation titled 'Physical AI Use Cases and Failure Factors and Improvement Strategies in the Process of Introducing RAG Agents'.
As interest in AI agents has recently increased, more companies are considering adopting AI. It is understood that many institutions and companies have already piloted and operated AI.
However, unfortunately, surveys have found that many companies and institutions either fail to achieve the desired ROI or find that functions that worked during PoCs fail to provide proper answers or operate correctly in the main project or on-site environments.
This information has made many companies hesitant to adopt AI hastily.
In this lecture, CLEVI CEO Lee Hwan-ho explained in detail the limitations of RAG systems, a leading cause of hasty AI adoption failures, as well as open-source models and small LLMs (SLMs), explaining why previous adoption cases had failed.
Ultimately, the reason for using AI is the expectation that it will take over people's tasks or deliver better performance than people.
As practitioners at companies that have adopted AI today do not trust the quality of AI responses, complaints are emerging that their workload is actually increasing because they have to check the AI's responses one by one.
The reason lies in the fact that most companies use fine-tuned open-source SLM models as solutions.
Intuitively, one might think that AI will become more intelligent if it learns something new. However, when AI learns new knowledge, it instead loses its past memories—in other words, arbitrary parameter weights.
An even more serious problem is that no one knows what information or knowledge has been lost.
Since adopting AI is not something that requires only minimal resources, you must have conducted extensive validation from the PoC stage.
Why, then, did those issues not appear at this stage? This was, of course, because it had been retrained using the target information.
If you teach an AI that only knew numbers to learn the alphabet and then ask it only questions about the alphabet, it will naturally answer well. But in the field, do you need only models specialised in such specific domains?
Our operations face new environments and new data every day. However, AI trained by randomly destroying its existing knowledge cannot guarantee either versatility or accuracy.
Therefore, you need to possess a From-scratch Foundation large language model or have the technology to create one.
Because these models contain almost all the knowledge in the world, they provide not only high-quality answers but also greater accuracy.
For specialised domains, you can overcome the limitations of fine-tuned SLMs by using SFT(Supervised Fine-Tuning), or, for small amounts of data, an RAG system or CLEVI’s proprietary intelligence database system, Semantic Database technology.
The government also understands the importance of these foundation models and is providing extensive support.
This is the National Representative AI Project. The project’s biggest goal is likely to secure digital sovereignty.
CLEVI, too, was aware of the critical limitations of using open-source models, so we independently developed everything from data collection to architecture design and trained CLEVI’s models.
CLEVI’s models currently deliver performance that is in no way inferior even when competing with the world’s leading AI systems.
In particular, because we have our own models, CLEVI is not affected by the dependency issues that can arise when using external models.
AI adoption is now becoming a necessity rather than a choice. However, simply adopting the latest technology makes it difficult to achieve the desired results.
True innovation begins only when you accurately understand the limitations of technology and establish the optimal strategy for your organisation’s environment and goals.
With differentiated technological capabilities, including its proprietary large language models and Semantic Database, CLEVI is minimising the trial and error and risks that companies face during the AI adoption process.
We will continue to listen to the voices from the field and do our best to become an AI partner that more companies can trust. We hope you will build the true success of AI adoption together with CLEVI.