Overview
RagChat Internal Knowledge Utilization Screen
The left sidebar allows you to select AI models, search chat history, and start new chats. The main screen displays AI-generated responses based on internal documents
RagChat - RAG Application for Internal Knowledge
RagChat is a corporate Retrieval-Augmented Generation (RAG) application that transforms your existing internal documents into an AI-powered knowledge base accessible to every employee. Built entirely on AWS infrastructure and deployable in approximately 3 weeks, RagChat eliminates knowledge silos, accelerates information retrieval, and delivers reliable AI-generated answers grounded in your company's own data.
Key Benefits
- Use existing documents as-is: Manuals, regulations, proposals, and technical papers become instant AI answer sources. Additions and updates are managed in-house without special training.
- Data stays in your AWS environment: Internal data never leaves your account and is never used for AI model retraining. Enterprise-grade security is maintained at all times.
- Multi-AI model support: Switch between Claude (Anthropic), Amazon Nova, and ChatGPT (OpenAI) models by scenario while preserving conversation context across model changes.
- Flat-rate pricing per company: No per-user charges, making it ideal for company-wide deployment across all departments.
How It Works
RagChat operates in two simple phases:
- Knowledge Addition: Administrators place documents in a designated location. Supported formats include PDFs and images.
- Knowledge Utilization: Users ask questions from the RagChat interface. AI immediately provides answers by referencing internal knowledge, suppressing hallucinations and citing source documents for reliability.
No special training is required for end users.
Technical Architecture
RagChat is built on Amazon Bedrock, AWS's generative AI service platform, leveraging Amazon Bedrock Knowledge Bases as a fully managed RAG service. Key components include:
- AI Models: Claude 3.7 Sonnet, Claude Sonnet 4, Amazon Nova Lite, Amazon Nova Pro, and OpenAI GPT models
- Data Storage: Amazon Aurora
- Authentication: AWS Cognito with integration into existing authentication infrastructure
- Design: Responsive UI for PC and mobile with sidebar navigation, chat history, model selection, and mode toggle (internal data only vs. general AI)
Use Cases
- Knowledge silos: Prevent business disruption when key employees are absent by making their expertise available to everyone through AI.
- Internal information search: Eliminate time wasted searching for documents scattered across the organization.
- New employee onboarding: Provide instant access to institutional knowledge without requiring manual documentation efforts.
- Consistent inquiry handling: Guarantee uniform, high-quality answers to internal and external inquiries.
Proven Results
RagChat is trusted by enterprises including Kajima Corporation (major general contractor), where it improved field productivity by enabling conversational knowledge search with source citations, and Shinto Holdings Co., Ltd. (diversified management across recycling, apparel, real estate, and inbound), where it enhanced cross-departmental knowledge sharing with multilingual support in Japanese, Chinese, and English.
Highlights
- Deploy a fully functional enterprise RAG system in approximately 1 weeks on your own AWS environment. Your internal documents - manuals, regulations, proposals, and technical papers - become instant AI answer sources without any special training for end users. Data never leaves your AWS account and is never used for AI model retraining, ensuring enterprise-grade security
- Switch seamlessly between Claude (Anthropic), Amazon Nova, and ChatGPT (OpenAI) models based on your scenario while preserving full conversation context. Built on Amazon Bedrock and Amazon Bedrock Knowledge Bases, RagChat uses Amazon Aurora for data storage and AWS Cognito for authentication. The responsive UI supports PC and mobile with features including chat history, model selection, drag-and-drop document upload, and a mode toggle between internal data only and general AI.
Details
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Pricing
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Support
Vendor support
Support for RagChat
DYC provides end-to-end support from initial scoping through ongoing operation and maintenance of your RagChat deployment.
Engagement Milestones:
- Requirements Hearing (Free): DYC conducts a discovery session to assess your organization's needs. Output: a scoping document outlining deployment architecture, data sources, and success criteria.
- Environment Construction: DYC builds the RagChat infrastructure within your AWS account. Output: deployed environment ready for testing.
- Precision Testing and Final Adjustments: DYC validates retrieval accuracy, tunes configurations, and confirms acceptance criteria are met. Output: verified system with documented test results.
- Handoff and Launch: DYC delivers operational documentation including an admin guide and runbook, then transitions to post-launch support.
Total implementation timeline is approximately 3 weeks.
Buyer Responsibilities:
- Provide AWS account access and necessary IAM permissions
- Designate an AWS administrator and content owners to participate in scoping and testing
- Supply internal documents (PDFs, images) for knowledge base ingestion
- Validate retrieval accuracy during the precision testing phase
Post-Launch Support Scope: DYC supports configuration changes, data ingestion assistance, deployment troubleshooting, and general product usage questions. Customers may also request guidance on document updates and system maintenance.
Contact: Email: support@dyc.co.jp Website: https://dyc.jp/
For refund requests or billing inquiries, contact DYC directly via email.