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AI Integration
RAG Document SearchContract AnalysisClient Intake AI

법률 AI 통합

당신의 어소시에이트가 4시간 소비하는 작업을 AI는 8초에 처리합니다

6,600
Monthly Searches
AI for law firms keywords
80%
Research Time Saved
Semantic search vs manual
10K+
Documents Indexed
Contracts, briefs, filings
95+
Lighthouse Score
Performance target
What Legal AI Integration Actually Does — And What Your DMS Can't

Your associate opens the DMS at 9pm, types "non-compete," and gets nothing — because the clause you need says "restrictive covenant." Legal AI Integration fixes this. Your firm gets semantic search that understands meaning, not just keywords. It retrieves precedent clauses in seconds, drafts motion language from your existing work product, and qualifies intake leads while your team sleeps. Every response cites the exact document, page, and clause — no hallucinated case law. Your data stays in your infrastructure. The Claude API processes queries in memory without training on your files. Full audit logs for privilege review. Access controls at the document level. This isn't ChatGPT cosplaying as counsel — it's built for legal precision, connects to Clio and Smokeball, and typically pays for itself in recovered billable hours within 6–8 weeks.

프로젝트가 실패하는 이유

Associates routinely burn 3-4 hours hunting for precedent clauses by hand That's billable time -- real money -- gone to a task AI handles in under 10 seconds. And it's not just the cost. It's the opportunity cost of what that associate could've been doing instead.
Getting a new lead qualified takes 3 to 5 email exchanges back and forth Meanwhile, another firm responded in 4 minutes. Potential clients don't wait around -- they hire whoever got back to them first, and that's just the reality of how intake works now.
Billing descriptions written from memory at 11pm Friday are a problem You're reconstructing what happened Tuesday from a cryptic time entry that says "research." The result? Underbilling, vague narratives, and write-down disputes that your billing partner dreads having every month.
Manual contract review means reading line by line under deadline pressure And when you're tired and rushing -- which is always -- unusual or problematic clauses get missed. That's not a skills problem. That's a volume and fatigue problem.
When a senior associate leaves for a competing firm in Chicago, they take years of accumulated knowledge with them There's no institutional memory. Decades of legal work sit locked in unindexed file folders that nobody can meaningfully search through.
Your document management system finds documents containing the exact word you typed That's it. So if you search "non-compete" and the document says "restrictive covenant," you get nothing. Relevant work product missed because the terminology didn't match perfectly.

컴플라이언스

Contract Analysis RAG

We've ingested 10,000+ contracts into pgvector and the search holds up. You ask about non-compete terms, the system finds relevant clauses even when the underlying document uses different language entirely. Want to compare how non-competes have been structured across 40 deals? Done. And every result cites the specific document and page number.

Case Research Assistant

Query your own case law research and internal briefs in plain English. The AI surfaces relevant precedents, gives you a summary of each holding, and flags distinguishing factors between cases. All with citations you can actually verify before relying on them in court.

Client Intake Automation

AI handles the first conversation with a new lead on your website -- 24/7, not just during business hours. It captures case details, figures out whether the matter fits your practice areas, and routes it appropriately. By the time it hits your CRM, there's already a full case summary waiting for whoever picks it up.

Billing Description Generator

Drop in a time entry, get back a professional billing narrative that actually justifies the hours. It's pretty straightforward -- attorneys log what they worked on, AI converts cryptic shorthand into descriptions that hold up to client scrutiny. Fewer write-downs, less time reconstructing your own day.

Document Drafting

Template plus actual case context equals a real working first draft -- not a blank page. Pleadings, demand letters, briefs. The attorney's job becomes reviewing and refining rather than staring at a cursor. That's a fundamentally different workflow.

Knowledge Management

Every document, every brief, every internal memo gets indexed and searchable by meaning -- not just by filename or exact keywords. So when someone leaves the firm, the knowledge they accumulated doesn't walk out with them. It stays, accessible, forever.

우리가 만드는 것

Retrieve precedent clauses across 10,000 documents by concept, not keyword match

Your team stops burning 3–4 billable hours per day hunting for clauses manually

Generate draft motions and discovery responses from your existing work product

New leads get qualified responses in minutes instead of losing them to faster competitors

Qualify client intake leads with jurisdiction-specific questions before handoff

Billing descriptions write themselves from actual case activity, reducing write-downs

Analyze contracts for non-standard clauses and flag risk provisions automatically

Unusual contract terms get flagged before they surface in litigation two years later

Surface institutional knowledge from files created by attorneys no longer at the firm

Decades of accumulated legal work becomes searchable by meaning, not filename archaeology

Reconstruct billing narratives from cryptic time entries logged days earlier

One associate recovering 2 hours daily pays for the system within 6 weeks at market rates

우리의 프로세스

01

Document Audit

We start by cataloging your actual document library -- what's there, how it's organized, which practice areas it covers. Then we map out the highest-value search use cases specific to your firm and design the ingestion and indexing strategy around those priorities.
Week 1
02

RAG Architecture

Next comes the technical architecture: pgvector embedding pipeline design, chunking strategy, retrieval parameter tuning. We also work through security requirements and privilege considerations before a single document gets processed.
Week 2
03

Ingest and Index

Then we actually run it -- processing and embedding your full document library. We test search quality against known queries where we already have verified answers, so we can measure accuracy before attorneys ever touch the system.
Week 3-5
04

Build Workflows

Client intake chatbot, billing description generator, document drafting templates -- all configured and integrated with your practice management system. Each module is tested against your actual workflows, not generic demos.
Week 6-8
05

Training and Launch

Attorney training, workflow customization, and go-live. Plus 30 days of post-launch tuning and support, because the first month always surfaces edge cases worth fixing.
Week 9-10
Claude APIpgvectorSupabaseVercelClio APIResend

자주 묻는 질문

AI가 정말 계약서를 의미론적으로 검색할 수 있나요?

네. 우리는 당신의 문서를 pgvector 임베딩에 수집하여 시스템이 단어뿐만 아니라 의미를 이해하도록 합니다. "경쟁 금지 조항"을 검색하면 "제한적 계약" 또는 "퇴직 후 제한"이라고 말하는 문서도 표시합니다 -- 의미론적으로 같은 의미이기 때문입니다. 이것이 단순히 더 빠른 키워드 검색이 아닌 진정으로 유용한 이유입니다.

변호사-의뢰인 특권 문서에 안전한가요?

네 -- 그리고 이것은 우리가 대화하는 모든 로펌에서 나오는 질문입니다. 모든 데이터는 당신의 인프라에 남아 있습니다. Claude API는 메모리에서 쿼리를 처리하고 문서를 보유하지 않습니다. 아무것도 모델 훈련에 사용되지 않습니다. 감사 로그는 특권 검토에 사용 가능하며, 모든 사람이 모든 것을 볼 수 없도록 문서 수준 접근 제어를 구현할 수 있습니다.

몇 개의 문서를 색인화할 수 있나요?

우리는 10,000개 이상의 문서로 RAG 시스템을 구축했으며, 정직하게 말해서 규모 질문은 끊임없이 나옵니다. 중요한 점은: 50,000개의 문서는 500개와 동일하게 작동합니다. 검색 속도는 라이브러리 크기에 관계없이 2초 이내로 유지됩니다. pgvector 아키텍처는 성능 저하 없이 이를 처리합니다.

법률 AI 통합 비용은 얼마나 드나요?

의미론적 검색이 있는 계약 RAG 시스템은 문서 볼륨에 따라 $15,000-$25,000부터 시작합니다. 전체 제품군 -- 인테이크 자동화, 청구 설명 생성, 문서 작성 -- 은 $35,000-$60,000입니다. 대부분의 로펌은 2개월 이내에 청구 가능한 시간으로 회수합니다. 이것은 판매 라인이 아니라 실제 수치입니다.

어떤 사건관리 시스템과 연동되나요?

Clio, Smokeball, PracticePanther, MyCase, 맞춤형 시스템. 문서 관리 측면에서: SharePoint, NetDocuments, iManage. 시스템에 API가 있으면 연동합니다. 깔끔한 API가 없으면 보통 방법을 찾았습니다.

AI의 정확도는 어느 정도인가요?

모든 AI 응답은 당겨온 특정 문서와 구절을 인용하므로 변호사는 의존하기 전에 검증할 수 있습니다. 그리고 우리는 의도적으로 검색 매개변수를 재현율보다 정밀도로 조정합니다 -- 5개의 높은 관련성 결과가 50개의 막연히 관련된 결과보다 낫습니다. 변호사가 유용한 것과 그렇지 않은 것을 표시함에 따라 정확도는 계속 개선됩니다.

로펌은 어떤 AI를 사용하나요?

법률 회사는 법률 조사를 위한 ROSS Intelligence, 계약 분석을 위한 Kira Systems, 소송 분석을 위한 Lex Machina를 포함한 다양한 AI 도구를 사용합니다. 또한 Luminance 및 eBrevia와 같은 도구는 문서 검토 및 실사를 지원합니다. 이러한 기술은 로펌이 효율성을 개선하고, 오류를 줄이며, 데이터 기반 결정을 내리는 데 도움이 됩니다. AI가 계속 발전함에 따라 법률 실무에의 통합이 점점 더 흔해지고 있으며, 법률 서비스가 제공되는 방식을 재정의하고 있습니다.

AI의 30% 규칙이란 무엇인가요?

법률 맥락에서의 "30% 규칙"은 AI가 중대한 혼란 없이 특정 직업이나 업계 내에서 최대 30%의 작업을 자동화할 수 있다는 지침을 나타냅니다. 법률 실무에서 이는 AI가 문서 검토, 법률 조사, 계약 분석과 같은 작업을 효율적으로 관리할 수 있으며, 생산성을 향상시키고 인간 변호사가 더 복잡하고 전략적인 업무에 집중할 수 있음을 의미합니다. 이 규칙은 자동화와 인간 전문 지식 사이의 균형을 강조하여 AI가 법률 전문가를 대체하지 않고 지원하도록 합니다.

Legal AI From ,000
Contract RAG. Client intake. Document drafting. Fixed-price.
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