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RAG development is a service that builds AI systems connecting internal documents and knowledge bases with LLMs to generate accurate, information-grounded responses.
RAG development is a service that builds AI systems connecting internal documents and knowledge bases with LLMs to generate accurate, information-grounded responses.
RAG（Retrieval-Augmented Generation）RAG (Retrieval-Augmented Generation) is a technology where AI searches and references external data such as internal documents when generating responses, producing accurate and evidence-based answers. Used as an internal ChatGPT, it dramatically improves internal information search and sharing efficiency.
Do These Challenges Sound Familiar?
We support companies struggling with internal information sharing and knowledge management through RAG technology.
Can't Find Internal Information
Much time is spent searching for needed information, significantly lowering productivity.
Knowledge is Person-Dependent
Critical knowledge is lost when experienced employees leave. Tacit knowledge isn't being shared.
Manuals are Scattered
Documents are spread across SharePoint, Confluence, file servers, and other locations.
Existing Search Doesn't Work
Only keyword-matching results appear, with no semantic search capability. Finding information takes too long.
How RAG Technology Works
Generate accurate answers from internal documents in 4 steps.
Document Ingestion & Vectorization
We ingest internal documents (PDFs, Word, Excel, meeting notes, etc.) and convert them into vector data that AI can understand.
Understanding User Questions
When users ask questions in natural language, the AI deeply understands the intent and context behind the query.
High-Precision Relevant Document Search
Vector search retrieves semantically relevant documents with high precision, going beyond simple keyword matching.
AI Generates Context-Aware Responses
Based on retrieved documents, the AI generates accurate, evidence-based responses with source citations.
Service Details
We support building internal search systems using RAG technology across 4 pillars.
Internal ChatGPT Development
Build an AI chatbot trained on your internal documents. Anyone can access the information they need simply by asking questions in natural language.
Knowledge Base Development
Build a unified search platform integrating manuals, FAQs, meeting notes, and other internal documents. Eliminate information silos.
Existing System Integration
Seamlessly integrate with existing tools like SharePoint, Confluence, Notion, and Google Drive. Minimize deployment effort.
Security Compliance
On-premises/VPC deployment options keep internal data in-house while enabling AI-powered search. Fully compliant with your security policies.
Supported Platforms
Compatible with major AI platforms and vector databases. We propose the optimal configuration for your environment.
Implementation Process
PoC starts in as little as 2 weeks. Phased implementation minimizes risk while delivering reliable results.
Requirements Interview & Data Assessment
1-2 weeksWe interview you about your business challenges, target data, and security requirements to design the optimal RAG architecture.
PoC & Prototype Development
2-4 weeksWe build a prototype using real data and validate search accuracy and practical usability.
Production Environment Build & Tuning
1-3 monthsWe set up the production environment and perform accuracy tuning, UI development, and existing system integration.
Launch & Continuous Improvement
OngoingAfter launch, we provide ongoing support for accuracy monitoring, data updates, and feature improvements.
Pricing Plans
From PoC to company-wide deployment. Phased implementation minimizes risk.
PoC (Proof of Concept)
Small-scale data validation and accuracy assessment. Verify the impact of RAG implementation with real data.
- Small-scale Data Validation
- Accuracy Assessment Report
- Prototype Development
- Implementation Decision Support
Standard
Department-level production deployment. Build a RAG system focused on specific business domains.
- Department-level Deployment
- Production Environment Setup
- Data Integration Configuration
- User Training
- 3-Month Maintenance Included
Enterprise
Company-wide deployment with multiple data source integration. Build a large-scale internal knowledge platform.
- Company-wide Deployment
- Multiple Data Source Integration
- Advanced Security
- Custom UI Development
- Dedicated Support
- SLA Guarantee
Monthly Maintenance & Operations
Accuracy improvement, data updates, and monitoring. Ongoing support for post-deployment operations.
- Accuracy Monitoring
- Regular Data Updates
- System Monitoring
- Inquiry Support
Permissions, audit trail, and how we measure accuracy
With RAG over internal data, deciding who can see what matters as much as answer quality.
Source document permissions are respected
Documents a user cannot open in SharePoint, Google Drive, etc. are excluded from their search results and answers.
Question and source logging (audit trail)
We record who asked what and which documents each answer was based on, so it can be reviewed later.
Data stays inside your environment
You can choose a setup that runs inside your own cloud, such as Azure OpenAI Service or Amazon Bedrock.
We measure accuracy before and after, with the same question set
We have our own evaluation setup that measures retrieval quality with recall, precision, and MRR. We build a question set from your real questions and report results measured with the same set before and after implementation. We do not promise a fixed number.
Frequently Asked Questions
Answers to common questions about RAG and internal AI search.
QWhat is RAG?
ARAG (Retrieval-Augmented Generation) is a technology that enhances AI response generation by searching and referencing external data sources like internal documents. This enables accurate, evidence-based responses that go beyond what LLMs like ChatGPT can know on their own, leveraging your company's proprietary information.
QIs internal data security ensured?
AYes, security is our top priority in design. We use enterprise-grade platforms such as Azure OpenAI Service and Amazon Bedrock, with data processed within your cloud environment. On-premises deployment is also available, ensuring data never leaves your organization.
QWhat document formats are supported?
AWe support major document formats including PDF, Word, Excel, PowerPoint, text files, HTML, and Markdown. We also support direct ingestion from cloud storage services like SharePoint, Confluence, Notion, and Google Drive.
QHow long does a PoC take?
AA PoC can start in as little as 2 weeks. For small datasets (several hundred documents), accuracy validation can be completed in 2-4 weeks. Timelines vary depending on data readiness and scale, so please feel free to contact us for a consultation.
QCan it integrate with existing SharePoint or Confluence?
AYes, integration is available with major business tools including SharePoint, Confluence, Notion, Google Drive, Box, and Slack. AI-powered cross-search can be added without changing your existing document management practices.
QHow accurate is it?
AIt depends on data quality, volume, and how documents are written, so we do not promise a fixed number. We build a question set from your real questions and measure retrieval quality with recall, precision, and MRR. In the PoC we compare before and after using the same question set, and you decide on production based on those results.
QHow are data updates handled after deployment?
AWe build mechanisms for automatic or scheduled vector database updates when new documents are added. Our Monthly Maintenance Plan provides ongoing support for regular data updates, accuracy monitoring, and continuous improvement.
QHow does it differ from other AI search tools?
AUnlike generic AI search tools, our solution offers full customization to match your business requirements. We deliver enterprise-grade quality including data source selection, search accuracy tuning, UI customization, and security compliance.
For additional questions or to discuss the best RAG configuration for your company, please contact us.
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AI Internal Search System (RAG Implementation) Selection Guide
A detailed guide on how to choose AI Internal Search System (RAG Implementation) providers, comparison points, and recommended companies.
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Ready to Dramatically Improve Internal Information Sharing?
Build an environment where anyone can instantly access information with an internal ChatGPT powered by RAG technology. Contact us for a free consultation.
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