Korean-language AI engineering and RAG system development
AI that truly understands language — and transforms your business
NLP & LLM (Natural Language Processing & Large Language Model) development is a specialist R&D service that turns enterprise language data into business value through GPT-4o-, Claude-, and Llama 3-based fine-tuning, RAG system development, text and sentiment analysis, and prompt engineering. TreeSoop’s KAIST- and POSTECH-trained engineers build domain-specific language AI using state-of-the-art fine-tuning techniques such as LoRA, QLoRA, and PEFT alongside Korean-specialized embedding models — rated 4.92/5 on Wishket, Korea’s largest dev-outsourcing platform (top 0.1% partner).
In specialist domains like legal, healthcare, and finance, general-purpose LLMs misread terminology, misunderstand context, and hallucinate — making them unreliable for real work.
Customer reviews, support transcripts, contracts, and reports pile up — but without a way to analyze and act on them, their latent value goes to waste.
Even with the same LLM, output quality swings dramatically with prompt design — and without systematic prompt-engineering know-how, performance stays inconsistent.
We fine-tune GPT, Llama, Mistral, and other models on domain-specific data, dramatically improving terminology comprehension, response style, and task accuracy.
We vectorize your internal documents, databases, and knowledge bases into a RAG pipeline the LLM searches and cites in real time — minimizing hallucinations while keeping answers current.
We build analysis pipelines that automatically extract sentiment, intent, and keywords from customer reviews, support logs, and social media data — turning raw text into business insight.
Systematic prompt engineering — Chain-of-Thought, few-shot, and system-prompt design — maximizes LLM performance while cutting cost.
We structure domain entities and relationships as ontologies and knowledge graphs, designing GraphRAG grounding that answers relational, multi-hop queries with accurate evidence — beyond the limits of vector-only RAG.
We collect the training and retrieval data — domain documents, conversation logs, labeled datasets — and assess its quality.
We select the optimal method — fine-tuning, RAG, prompt engineering, or a combination — weighing cost, performance, and maintainability.
Starting from a baseline model, we improve performance through iterative experiments, evaluated objectively against benchmark datasets.
The finished model and pipeline are wrapped in a REST API and integrated into your existing services.
We maintain quality through production performance monitoring, drift detection, and scheduled retraining.
A legal AI that indexes case law, statutes, and contracts in a vector DB, retrieving relevant legal information instantly from natural-language queries
A chatbot fine-tuned on the retailer’s customer-service conversations that automatically handles return, exchange, and shipping inquiries
A RAG system combining halal regulatory documents with an ingredient database to automatically screen food ingredients for halal compliance
90%+
Domain-specific accuracy
Performance gain over general-purpose models after fine-tuning
5x
Faster retrieval
RAG vector search vs. full-text keyword search
65%
Hallucination reduction
Drop in hallucination rate after RAG adoption
30%
LLM cost savings
After prompt optimization and caching
Our experts will recommend the best approach for your project.
