Cut Information Retrieval Time by 85% with Internal Document RAG

Legal Case & Contract Search for Law Firms

ChallengeLawyers spent an average of 4 hours per case reviewing thousands of precedents and past contracts to find similar cases

SolutionHypemarc's RAG system learned the firm's entire library of precedents, contracts, and legal advisory history, enabling instant natural-language search

Results90% reduction in case search time, 70% faster contract drafting, 98% lawyer satisfaction

Consulting Firm Project Knowledge Management

ChallengeConsultants spent 10 hours per week searching for past project reports and industry research across multiple systems or asking colleagues

SolutionHypemarc's RAG system learned past project reports, industry analyses, and strategy frameworks, providing relevant insights and sources in response to consultant queries

Results80% reduction in knowledge search time, improved project quality, 50% shorter onboarding for new consultants

R&D Technical Documentation & Patent Search

ChallengeResearchers failed to leverage existing know-how or avoid duplicate research due to the sheer volume of technical documents and patents

SolutionHypemarc's RAG system learned technical documents, patents, and experiment notes, recommending related documents and similar patents in response to research queries

Results30% R&D cost savings by preventing duplicate research, 60% increase in technical document reuse, improved patent filing quality

Enterprise Internal Policy & Manual Search

ChallengeEmployees across the organization spent thousands of hours annually searching the intranet or contacting HR for policy and procedure information

SolutionHypemarc's RAG system learned HR policies, benefits guides, and operational manuals, providing precise answers and related documents in response to natural-language questions

Results75% reduction in HR inquiries, 85% faster information retrieval for employees, improved policy compliance

1. 01

   Document Landscape Assessment

   Identify internal document types and formats (PDF, Word, PPT, etc.) and review access permissions and security policies.

2. 02

   Document Collection & Preprocessing

   Collect documents from internal systems (SharePoint, Confluence, etc.) and extract/refine text using OCR and parsing.

3. 03

   Vector Embedding & Database Construction

   Convert documents into AI-readable vectors, store in a Vector DB, and build the RAG search pipeline.

4. 04

   Pilot Testing & Optimization

   Beta test with a select team to verify search accuracy and refine the system based on user feedback.

1. 011 week

   Document Environment Analysis

   Assess internal document landscape, analyze formats, review access policies, define RAG scope

2. 022 weeks

   Document Collection & Preprocessing

   Collect documents from internal systems, extract text via OCR & parsing, refine metadata, verify quality

3. 032 weeks

   Vector DB & RAG Construction

   Generate document embeddings, build Vector DB, develop RAG search pipeline, integrate access control

4. 041 week

   Pilot Testing

   Beta test with select teams, verify search accuracy, collect user feedback, optimize system

5. 05Ongoing

   Company-Wide Deployment & Maintenance

   Full organizational rollout, auto-update new documents, analyze usage patterns via Hypemarc.AI dashboard, continuous improvement

Total Timeline · 6 weeks

What document formats are supported?

We support most document formats including PDF, Word, PPT, Excel, images (JPG, PNG), and text files. OCR technology also extracts text from scanned documents and images.

How is RAG different from regular search?

Traditional search finds documents via keyword matching. RAG understands the meaning of your question, retrieves relevant documents, and uses an LLM to generate precise answers based on those documents. This minimizes hallucination and provides clear source attribution.

How is document security maintained?

Hypemarc's RAG system enforces strict per-user access controls. If a user lacks permission to the original document, it won't appear in search results. All data is encrypted at rest, and on-premise deployment is available.

How are new documents updated?

Hypemarc auto-syncs with internal systems (SharePoint, Confluence, etc.) to update new or modified documents in real time or on a schedule. Manual uploads are also supported.

How is search accuracy guaranteed?

Hypemarc's RAG system generates answers based on actual document content, achieving 95%+ accuracy. Domain-specific tuning during the pilot phase further improves precision.

What does it cost to implement?

Costs vary by document volume and user count. SMBs can typically access affordable monthly subscription models, while enterprises can opt for on-premise or dedicated cloud deployments. Contact us for a free consultation and custom quote.