RAG 시스템
귀사의 데이터, AI 정확성
검색 증강 생성 grounds AI responses in your proprietary documents, databases, and knowledge bases. 아니오 hallucinations—just accurate, source-cited answers from your actual data.
RAG가 모든 것을 바꾸는 이유
스탠다드 LLMs are trained on public data and can hallucinate. RAG retrieves relevant context from YOUR data before generating responses—dramatically improving accuracy and relevance.
RAG 시스템 구성요소
문서 처리
Ingest documents from any source—PDFs, Word, web pages, databases, APIs. Our pipeline handles OCR, table extraction, and intelligent chunking.
벡터 데이터베이스
Documents are converted to embeddings and stored in high-performance vector databases optimized for semantic similarity search at scale.
검색 및 생성
Queries are embedded, relevant chunks retrieved, and context injected into LLM prompts. Responses cite sources and indicate confidence.
⚡ 빠른 견적 요청
Tell us about your knowledge base needs.
RAG 응용
엔터프라이즈 지식 관리
Instead of hallucinating facts, the AI only uses information from your approved documents. No made-up statistics.Ask questions, get accurate answers
법률 연구
검색 contracts, precedents, and regulations with semantic understanding. Find relevant clauses across thousands of documents in seconds.
의료 문서화
Query patient records, clinical guidelines, and research papers while maintaining HIPAA compliance and audit trails.
기술 문서화
Provide instant answers from API docs, user manuals, and knowledge bases. Reduce support tickets by 60%+.
재무 분석
Query earnings reports, SEC filings, and market research. Extract insights across years of financial data.
엔터프라이즈 RAG 기능
🔐 접근 제어
Document-level permissions ensure users only retrieve content they're authorized to access. Integrates with SSO and existing IAM.
🔄 Real-Time Sync
연결 to SharePoint, Confluence, Google Drive, and more. Documents are automatically indexed when created or updated.
🌐 Multi-Language
Support for 베트남ese, 한국n, Chinese, Japanese, and 50+ languages. Cross-lingual retrieval finds relevant content regardless of query language.
📊 분석
Track what questions users ask, what documents are retrieved, and where knowledge gaps exist. Continuous improvement insights.
지식 베이스를 구축할 준비가 되셨나요?
We'll assess your documents, recommend architecture, and deliver a working RAG system in 4-8 weeks.