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RAG knowledge management is a service that organizes and makes your organization's knowledge assets searchable with AI, improving information utilization efficiency across the entire organization.
RAG knowledge management is a service that organizes and makes your organization's knowledge assets searchable with AI, improving information utilization efficiency across the entire organization.
What Is AI Internal Knowledge Platform Development (RAG Implementation)
AI internal knowledge platform development is a service that leverages RAG (Retrieval-Augmented Generation) technology to build systems that enable unified AI search across documents, manuals, FAQs, and policies scattered throughout your organization. Information that was previously impossible to find with traditional keyword search can now be accurately retrieved simply by asking in natural language, with answers supported by source documents. By combining vector search with LLMs (Large Language Models), we deliver highly reliable AI responses with minimal hallucination (incorrect answers) based on your internal information. Radineer provides consistent support from data surveys to PoC, production deployment, and operational improvement, delivering optimal RAG systems backed by our expertise from over 15 implementations.
Industries we support
Service Flow
Data Survey & Requirements Definition
- Target data source inventory
- Data quality and volume assessment
- System requirements definition
- Architecture design
RAG Infrastructure Development & Tuning
- Vector DB development and embedding generation
- RAG pipeline implementation
- Search accuracy tuning
- Chat UI development
Operations & Continuous Improvement
- Data update automation
- Accuracy monitoring and improvement
- Usage analytics dashboard
- User feedback integration
Pricing Plans
We propose the optimal plan for your challenges
PoC/Validation Plan
RAG validation and accuracy evaluation
- Target data survey and organization
- RAG prototype development
- Accuracy validation report
- Implementation roadmap development
Standard Plan
Department-level RAG infrastructure development
- Vector DB design and development
- Automated internal document ingestion
- Chat UI implementation
- Access control and security configuration
- Accuracy tuning
Enterprise Plan
Company-wide deployment with multi-source integration
- Multiple data source integration
- SSO and AD integration
- Multi-tenant support
- Dashboard and analytics features
- SLA guarantee with dedicated support
Operations & Improvement Plan
Continuous accuracy improvement and operational support
- Automated data update configuration
- Accuracy monitoring
- Monthly reports and improvement proposals
- User support
Success Stories
From challenges to results—specific improvement case studies
Technical documents and design specs were scattered across multiple systems, taking an average of 40 minutes to find the needed information
Built an AI-powered internal technical knowledge search system using RAG. Integrated SharePoint, file servers, and Confluence for natural language cross-platform search
Internal FAQs and manuals were underutilized, with help desk inquiries exceeding 500 per month
Built an internal FAQ AI chatbot. Trained on employee policies, expense procedures, and IT instructions for 24/7 automated responses
Checking laws, regulations, and internal rules was time-consuming, and compliance checks were dependent on individual expertise
Built a legal and compliance-specialized RAG system. Integrated regulatory databases with internal policies to generate evidence-backed answers
Past proposals and project insights were not being accumulated as knowledge, forcing teams to create materials from scratch each time
Built a sales knowledge search AI. Vectorized past proposals, quotes, and deal records to enable instant search of similar cases
Comparison with Others
Why Radineer Is Chosen
| Item | Radineer | Others |
|---|---|---|
| RAG Development Track Record | ◎ | △ |
| Vector Search Accuracy | ◎ | ○ |
| Multi-Source Support | ◎ | △ |
| Security Measures | ◎ | ○ |
| Custom UI Development | ◎ | × |
| AI Expertise | ◎ | △ |
| Operations & Improvement Support | ◎ | × |
Lead Consultant
Supported by experienced specialists
Yuichi Kita
Managing Partner / CEO
Over 10 years of experience in SEO and digital marketing. Track record of supporting 200+ companies from enterprises to SMBs. Pioneer in AI utilization and LLMO strategies.
Keiichi Eto
Executive Partner
Former major SEO company executive. Handled SEO improvements across a wide range of industries including e-commerce, media, and B2B services.
Frequently Asked Questions
Q.What is RAG?
RAG (Retrieval-Augmented Generation) is a technology that retrieves relevant information from external databases and documents, then uses that information as the basis for AI-generated responses. By using internal documents as a data source, you can build an AI system that accurately answers questions specific to your organization.
Q.What is RAG?
Regular AI chatbots (like ChatGPT) respond based on pre-trained general knowledge and cannot answer questions about company-specific information. RAG searches internal documents in real-time to generate responses, so it can accurately answer company-specific questions like 'What are our expense reimbursement rules?' with supporting documentation.
Q.RAG (Retrieval-Augmented Generation) is a technology that retrieves relevant information from external databases and documents, then uses that information as the basis for AI-generated responses. By using internal documents as a data source, you can build an AI system that accurately answers questions specific to your organization.
RAG (Retrieval-Augmented Generation) is a technology that retrieves relevant information from external databases and documents, then uses that information as the basis for AI-generated responses. By using internal documents as a data source, you can build an AI system that accurately answers questions specific to your organization.
Q.How long does implementation take?
PoC takes 2-4 weeks, Standard Plan 1-2 months, and Enterprise Plan 2-4 months as a guideline. Timelines vary based on data readiness and security requirements. Phased implementation is also possible, and we recommend starting with a PoC.
Q.Is it secure?
We support on-premises and private cloud deployments, with configurations that keep data from leaving your organization. Standard features include access control (SSO/AD integration), audit logs, and encrypted communications. We have implementation experience with financial institutions and healthcare organizations.
Q.What is the difference between a regular AI chatbot and RAG?
We support major formats including PDF, Word, Excel, PowerPoint, HTML, Markdown, and text files. OCR extraction of text from images is also supported. Integration with major platforms including SharePoint, Confluence, Google Drive, and Notion is available.
Q.Regular AI chatbots (like ChatGPT) respond based on pre-trained general knowledge and cannot answer questions about company-specific information. RAG searches internal documents in real-time to generate responses, so it can accurately answer company-specific questions like 'What are our expense reimbursement rules?' with supporting documentation.
Regular AI chatbots (like ChatGPT) respond based on pre-trained general knowledge and cannot answer questions about company-specific information. RAG searches internal documents in real-time to generate responses, so it can accurately answer company-specific questions like 'What are our expense reimbursement rules?' with supporting documentation.
Q.Can it integrate with existing systems?
Yes. We can integrate via API with major systems including SharePoint, Confluence, Google Drive, Box, Notion, internal wikis, Slack, and Teams. We also support embedding RAG functionality into existing internal portals and chat tools.
Q.Can small organizations implement this?
Yes, absolutely. You can start with the PoC/Validation Plan (from JPY 500,000), beginning with a small team or specific department and expanding company-wide after confirming results.
Q.How much does implementation cost?
Data additions and updates are automated, so no dedicated operations staff is required. With the monthly Operations & Improvement Plan, Radineer handles accuracy monitoring, improvement proposals, and user support. We also support internal AI adoption initiatives.
AI Internal Knowledge Platform Development Selection Guide
A detailed guide on how to choose AI Internal Knowledge Platform Development providers, comparison points, and recommended companies.
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Ready to Leverage Internal Knowledge with AI?
With RAG development expertise from 15+ implementations, we dramatically improve internal information search and utilization. Contact us for a free consultation.