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많은 기업이 이러한 문제를 해결하기 위해 RPA, OCR 및 여러 워크플로 도구에 의존해 왔지만 이들 솔루션은 유연성이 없고 유지 관리 및 확장이 복잡하며, 개별 비즈니스 팀에서 독립적으로 사용되는 경우가 많습니다.\u003C/p\u003E\r\n\u003Cp\u003E이에 대한 해결책은 비용을 10배 절감할 수 있는 AI 기반 자동화에 있습니다. 그러나 이러한 문서의 복잡성과 가변성은 중대한 과제로 이어지며, 풍부하게 구조화된 문서가 기본 도구를 통해 평면 텍스트로 처리되면 핵심 비즈니스 컨텍스트가 손실되어 분석 및 AI의 효과를 떨어뜨립니다.\u003C/p\u003E\r\n\u003Cp\u003E기업이 이와 같은 문제를 극복하려면 문서에 저장된 관련 정보를 손쉽고 정확하게 자동 추출할 수 있는 중앙 집중식 플랫폼을 제공하는 지능형 문서 처리(IDP) 시스템 또는 Document AI가 필요합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-978148ac5c","alt":"document ai","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--716d765d-cad7-42d7-902d-e6dd8f4afaf0/screenshot-2025-08-29-at-7.08.32%25E2%2580%25AFam.png?preferwebp=true&quality=85","width":"2558","height":"1109",":type":"snowflake-site/components/image"},"blog_text_725766779":{"id":"blog-text-93cd9cca2d","text":"\u003Ch2\u003ESnowflake에서 Document AI를 활용한 엔드투엔드 지능형 문서 처리 실현\u003C/h2\u003E\r\n\u003Cp\u003ESnowflake는 문서 지능을 위한 포괄적인 엔드투엔드 플랫폼을 제공하며, 이는 AI 데이터 클라우드에 원활하게 통합되어 있습니다. 조직은 이를 통해 수집 및 추출에서 검증 및 애플리케이션에 이르기까지 문서 처리의 전체 수명 주기를 안전하며 거버넌스가 적용된 단일 환경 내에서 관리할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E이 생태계의 핵심 구성 요소는 \u003Cb\u003ESnowflake Cortex AI\u003C/b\u003E로, 지능형 애플리케이션을 위한 기본 구성 요소를 제공합니다. 이에는 다음 주요 기능이 포함됩니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E구성 가능한 프레임워크\u003C/b\u003E: Snowflake 네이티브 인터페이스, 함수, LLM 및 Python 로직을 결합하여 맞춤형 워크플로우를 제공합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E엔드투엔드 수명 주기 지원\u003C/b\u003E: Snowflake 내에서 문서 데이터를 수집, 추출, 검증 및 적용합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E다중 형식 호환성\u003C/b\u003E: PDF, 이미지, 손글씨 스캔 등 10개 이상의 형식을 변환 없이 처리합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E정형 및 비정형 데이터 융합\u003C/b\u003E: 문서 데이터를 정형화된 Snowflake 소스와 직접 일치시킵니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E멀티 모달 지능\u003C/b\u003E: 텍스트, 레이아웃, 테이블 및 이미지를 함께 사용하여 완전하고 정확한 이해를 돕습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E사람의 검토 및 승인 지원\u003C/b\u003E: 검토 워크플로우, 예외 처리 및 검증 로직을 손쉽게 추가할 수 있습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E사용자 지정 모델 훈련\u003C/b\u003E: 추출 모델을 파인튜닝하거나 통합 지원이 포함된 사전 훈련된 모델을 사용할 수 있습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E네이티브 배포, 중앙 관리\u003C/b\u003E: Snowflake의 내장형 보안 및 옵저버빌리티로 플랫폼 내에서 모든 것을 실행합니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1084550262":{"id":"image-48c42bbd84","alt":"ingest-orchestration-production","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1820b631-dd45-4537-8c38-bd5d76b74158/screenshot-2025-08-29-at-7.12.48%25E2%2580%25AFam.png?preferwebp=true&quality=85","width":"2313","height":"943",":type":"snowflake-site/components/image"},"blog_text_127231164":{"id":"blog-text-1c418ddb08","text":"\u003Ch2\u003E주목할 만한 새로운 혁신\u003C/h2\u003E\r\n\u003Cp\u003ESnowflake는 최근 문서 처리 기능을 향상시켜 조직이 문서 지능을 다음 단계로 끌어올릴 수 있도록 지원하고 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EAI_EXTRACT\u003C/b\u003E는 새로운 AISQL 함수로, 문서, 이미지 및 텍스트에서 정보를 추출하는 가장 효율적이고 확장 가능한 방법을 제공합니다. 또한, \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/en/engineering-blog/arctic-extract-vision-language-document-ai/\" target=\"_blank\"\u003ESnowflake의 최신 독점 모델인 Arctic-Extract\u003C/a\u003E에 의해 구동되며, 29개 언어를 지원합니다. 자세한 내용은 해당 링크를 참조하세요. \u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPARSE_DOCUMENT LAYOUT\u003C/b\u003E 모드는 추출 중에 복잡한 비즈니스 문서가 가진 풍부한 구조적 특성을 보존하여 엔터프라이즈 RAG를 개선합니다.\u003C/li\u003E\r\n\u003Cli\u003EDocument AI의 \u003Cb\u003ETable Extraction\u003C/b\u003E은 이제 문서 내 테이블에서 정형화된 컬럼 기반 데이터를 추출하여 즉시 분석할 수 있도록 지원합니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E각 항목에 대해 좀 더 자세히 살펴보겠습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch2\u003E문서 처리 파이프라인을 프로그래밍 방식으로 대규모로 구축 및 실행\u003C/h2\u003E\r\n\u003Cp\u003E마침내 공개된 AI_EXTRACT는 다양한 비정형 데이터를 엔터프라이즈 규모의 정형화된 형식으로 변환하는 SQL API 추론 솔루션입니다. 이를 통해 텍스트, 이미지 및 문서와 같은 소스에서 정형화된 정보를 추출하고, 해당 정보를 효율적인 분석을 위해 표준 형식으로 통합할 수 있습니다. \u003C/p\u003E\r\n\u003Cp\u003EAI_EXTRACT는 이미지, 텍스트 및 레이아웃 정보를 단일 패스로 처리하여 추론 및 훈련 시간을 단축하는 Snowflake의 차세대 문서 이해 모델인 \u003Cb\u003EArctic-Extract\u003C/b\u003E를 기반으로 구동됩니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_871851002":{"id":"image-71f4280648","alt":"artic-extract","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--176ddc74-dab5-4e77-b455-e40b9687ad6a/screenshot-2025-08-29-at-7.12.17%25E2%2580%25AFam.png?preferwebp=true&quality=85","width":"1784","height":"943","title":"This chart displays a performance comparison of seven Intelligent Document Processing solutions on the DocVQA (Document Visual Question Answering) benchmark.",":type":"snowflake-site/components/image"},"blog_text_1277816592":{"id":"blog-text-6a0052bc91","text":"\u003Cp\u003EAI_EXTRACT의 API 우선 접근 방식은 "코드형 인프라(Infrastructure as a Code)"를 가능하게 하여 사용자는 UI 없이도 주어진 문서에 대한 데이터 추출을 프로그래밍 방식으로 수행하고 동적으로 추출 프롬프트를 정의할 수 있습니다. 이를 통해 다양한 공급업체의 인보이스와 같은 서로 다른 형식의 문서를 처리할 수 있는 유연성을 확보할 수 있습니다. 이뿐만 아니라 29개 언어에 대한 지원을 비롯하여, 날짜 및 통화와 같은 변수 데이터 형식의 지능형 정규화 등이 추가 기능으로 제공됩니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch2\u003E복잡한 멀티 모달 문서에 대한 레이아웃 인식 구문 분석\u003C/h2\u003E\r\n\u003Cp\u003E대차대조표와 같은 재무 문서를 분석할 때, 테이블이나 컬럼에 있는 숫자의 \u003Ci\u003E컨텍스트\u003C/i\u003E를 이해하는 것이 중요합니다. 기존의 문서 처리의 경우 데이터 요소를 올바르게 추출할 수는 있지만, 부채 조건이나 이자율을 자세히 설명하는 중요한 각주를 완전히 놓칠 수 있습니다. 이로 인해 \u003Ci\u003E장기\u003C/i\u003E \u003Ci\u003E부채\u003C/i\u003E와 같은 항목과 해당 숫자를 정당화하는 설명 간의 필수적인 연결이 끊어집니다. 이러한 문제는 분석이나 추출된 값을 사용하는 AI 시스템의 기능을 표면 수준 데이터로만 제한합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPARSE_DOCUMENT LAYOUT\u003C/b\u003E 모드는 이와 같은 과제를 해결하고자 특별히 설계되었습니다. 테이블, \u003Cb\u003E이미지 또는 다른 복잡한 레이아웃\u003C/b\u003E이 포함되어 있더라도 문서의 레이아웃을 정확히 유지함으로써 필요한 정보와 관련 컨텍스트를 이해할 수 있습니다. 이는 아래 예시에도 잘 나타나 있습니다. 복잡한 테이블을 포함하는 SEC 문서의 무결성이 처리 중에도 유지되는 것을 알 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_547445999":{"id":"image-798f2cf9a3","alt":"parse","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b058d73c-56d4-43fb-9687-aeb1b7c01a1f/screenshot-2025-08-29-at-7.10.13%25E2%2580%25AFam.png?preferwebp=true&quality=85","width":"1697","height":"877",":type":"snowflake-site/components/image"},"blog_text_1308805110":{"id":"blog-text-f6415a3d9d","text":"\u003Cp\u003E결과적으로 단순한 데이터 검색(RAG)을 넘어 문서에 대한 \u003Cb\u003E심층적인 분석 질의\u003C/b\u003E를 수행할 수 있습니다. 총 자산의 가치에 대해 묻는 대신, 다음과 같이 훨씬 더 구체적인 질문을 할 수 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E“'부채' 테이블에 나열된 채무 상품의 만기일과 이자율은 무엇인가요?”\u003C/li\u003E\r\n\u003Cli\u003E"주석에 따르면, 이번 분기에 '영업권'이 증가한 주요 원인은 무엇이었나요?”\u003C/li\u003E\r\n\u003Cli\u003E“손익계산서 앞에 있는 본문 내용에 설명된 대로 회사의 수익 인식 회계 정책을 요약하세요.”\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch2\u003E문서에서 정확한 테이블 추출 \u003C/h2\u003E\r\n\u003Cp\u003E정보에 기반한 비즈니스 결정을 내리려면 계약서, 인보이스 및 기타 재무 제표와 같은 복잡한 문서를 분석해야 합니다. 일반적인 예는 매년 발행되는 10-K 보고서로, 복잡한 테이블에 상세한 재무 성과 데이터를 포함하고 있어 자동 추출은 큰 과제로 여겨집니다. 해당 데이터를 수동으로 추출하는 것은 느리고 오류가 발생하기 쉬운, 리소스 집약적인 프로세스입니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake \u003Cb\u003EDocument AI\u003C/b\u003E는 새로운 \u003Cb\u003ETable Extract\u003C/b\u003E 기능으로 이 과제를 정면 돌파합니다. 2025 세계 경제 전망 업데이트를 예로 들어 보겠습니다. 이 문서에는 거의 동일한 구조로 구성된 여러 테이블이 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E아래 이미지에서 볼 수 있듯이, Document AI는 문서에서 올바른 테이블을 식별하고 중첩된 헤더와 행이 있는 경우에도 모든 데이터를 정형화된 형식으로 추출하는 \u003Cb\u003E제로샷 추출\u003C/b\u003E을 수행합니다. 해당 기본 모델은 이러한 복잡한 레이아웃을 파인튜닝 없이 처리할 수 있을 만큼 강력합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1947508327":{"id":"image-570e4e362c","alt":"world-economic","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f87e1938-99f7-43fb-a708-df5cd9c237c3/screenshot-2025-08-29-at-7.11.04%25E2%2580%25AFam.png?preferwebp=true&quality=85","width":"1937","height":"1199",":type":"snowflake-site/components/image"},"blog_text_426275347":{"id":"blog-text-be48a9501a","text":"\u003Cp\u003E제로샷 추출뿐만 아니라, \u003Cb\u003E스키마 기반 추출\u003C/b\u003E을 활용하면 스키마를 정의하고 자연어로 원하는 컬럼을 지정할 수 있습니다. 유사한 형식을 가진 여러 테이블이 포함된 문서의 경우, ‘Locator’ 필드를 사용하여 올바른 테이블을 고유하게 식별하고 타겟팅할 수 있습니다. 마지막으로, Document AI의 Table Extract를 통해 모델의 \u003Cb\u003E주석 작업 및 파인튜닝\u003C/b\u003E을 수행하여 추출 정확도를 향상시킬 수도 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch2\u003ESnowflake에서 지능형 문서 처리 시작하기\u003C/h2\u003E\r\n\u003Cp\u003E복잡한 문서 처리는 더 이상 느리고 오류가 발생하기 쉬우며 리소스 집약적인 수동 작업이 아닙니다. 비즈니스 컨텍스트를 고려하지 않는 기존의 경직된 자동화 솔루션은 이제 구시대의 유물입니다. 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