RAG & Internal Knowledge Search AI
Let AI find and use your company’s knowledge.
You have many accumulated documents, but finding the information you need is hard. Even when important knowledge is contained in work manuals, technical documents, past reports, internal policies, and various materials, it often can only be used when someone finds and reads it directly.
RAG-based internal knowledge AI connects this information to AI, supporting employees to ask questions in natural language and quickly find what they need.
Beyond simply storing or searching documents, we build a knowledge environment where you can find relevant materials, generate answers, and verify the source documents.
01. How well are you using your company’s documents?
Companies have work knowledge accumulated over long periods. But as materials are scattered across folders and systems, or as the volume of documents grows, finding needed information takes more time and effort.
Internal knowledge AI is a way to reduce these problems.
When an employee asks in natural language, AI searches relevant documents and generates an answer based on the retrieved content. When needed, you can check the source to review the answer’s basis.
We support making the knowledge your company holds easier to find and use more efficiently.
02. What is RAG?
RAG (Retrieval-Augmented Generation) is a technology called retrieval-augmented generation.
A typical AI model answers based on knowledge it acquired during training. But it does not automatically know a specific company’s internal documents or the latest work guidelines.
To complement this limitation, RAG retrieves information relevant to the user’s question from an external knowledge store and passes it to the AI to generate an answer.
For example, an employee might ask:
AI searches relevant reports and technical documents and organizes the key problems and solutions based on the materials it finds. Depending on the system configuration, it can also provide the referenced documents and source locations.
The difference between general AI and RAG
General AI
Answers the user’s question based on knowledge the model has already learned and input information.
RAG-Based AI
First searches internal materials relevant to the user’s question, then generates an answer based on the retrieved content.
RAG is a way to use internal knowledge in answers without retraining the model. However, actual results vary by document quality, search performance, the model’s answering ability, and system design.
03. How internal knowledge AI works
Document Collection
Collect the data AI will use — work manuals, technical documents, reports, product materials, internal policies, and more.
Document Analysis & Cleaning
Extract text from documents and clean up unnecessary content. Long documents are split into search-friendly units, and tables, images, and document structure are handled separately when needed.
Knowledge Indexing
Vectorize document content with an embedding model and store it in a search system. You can manage metadata such as document name, page, date, and category together.
Relevant Information Retrieval
When a user asks a question, the search system finds the most relevant documents and content within them. Keyword and semantic search can be used together as needed.
AI Answer Generation
Pass the retrieved content to the AI model to generate an answer suitable for the question. When relevant materials are lacking, it can be configured to indicate that the basis is insufficient.
Source Verification & Quality Improvement
You can provide the documents and source locations used in the answer, and analyze real questions and results to improve search quality and answer accuracy.
Through this process, a company’s documents evolve from simple files into knowledge assets usable in real work.
04. RAG Setup & Consulting Services
Internal Document Integration & Data Collection
We analyze where and in what formats your documents are stored, and build a collection and cleaning system for AI search.
- Review of document format support (PDF, TXT, Markdown, Word)
- Review of extraction methods for HWP and other work documents
- Integration with internal file servers and designated document stores
- Document metadata & classification system setup
- Cleaning up duplicate documents and unnecessary data
- Automation of document changes and new material updates
- Data collection status & error management
Depending on document format and structure, separate conversion or OCR processing may be needed, and the actual setup scope is determined by the state of the source data.
Intelligent Search System Setup
RAG quality depends not only on the AI model but on how accurately it finds the information you need. We analyze document characteristics and question types to design the search structure and configure it to effectively find highly relevant materials.
- Document chunking strategy design
- Embedding model selection & application
- Vector database setup
- Keyword & semantic search configuration
- Review of hybrid search & result reranking
- Search filters by document type & metadata
- Search quality validation with real questions
We focus not just on building a vector database, but on validating how well it finds the information your real work needs.
AI Q&A Service Setup
We build an AI service that generates natural answers based on retrieved materials.
- Internal knowledge search chatbot
- Document-based Q&A
- Summarization of key content in long documents
- Integration of relevant information across documents
- Providing reference documents & sources
- Answer format & per-work response rules
- Response policy when relevant materials are lacking
To increase answer reliability, we can validate search results and generated answers and design it so content without evidence is not presented as definitive.
Local AI & On-Premises RAG Setup
We combine self-operated AI models with RAG to fit your security policy and infrastructure environment.
- Local LLM & embedding model configuration
- Internal server & intranet environment setup
- Vector database & document store integration
- External AI API usage & data transmission policy design
- Air-gapped document collection & model update plans
- Integration with existing internal systems
Using a self-operated environment lets you design a structure that limits data transmission to external AI services. However, the overall level of data protection depends on network, application, storage, and access permission settings.
Document Permissions & Operation Management
Internal knowledge AI should not be a system that exposes all documents to all employees. We design the searchable data scope considering existing work permissions and security policies, and build an operation system that can be managed continuously.
- Document access permissions by department & user
- Permission filtering of search results
- Index management for document add/modify/delete
- Data collection & search error monitoring
- Usage records & operation log management
- Answer quality & system performance improvement
Actual permission control must be implemented so that source document access permissions and the search system’s permission policy are applied consistently.
05. Usable across various work tasks
Internal Policy & Work Manual Search
“Tell me the travel expense settlement procedure and required documents.”
Search internal policies and work manuals to organize relevant procedures and needed content.
Technical Document & Incident Response Support
“Find past incident cases and remediation methods related to this error.”
Search technical documents and past incident records to provide similar cases and response information.
Project & Report Search
“Summarize the key problems and improvements from the last project.”
Find past reports and project materials to support summarizing and comparing key content.
Product & Customer Support
“What are the common problems with this product and how to solve them?”
Search relevant information based on product manuals and technical materials, and generate answers usable for customer response.
New Employee Onboarding Support
“When I first take on this work, which procedures should I check first?”
Search work guidelines and related materials to guide needed information. It can help reduce the repetitive explanations owners used to give directly.
Development & Technical Knowledge Search
“Where is this program’s authentication procedure implemented, and what are the related documents?”
We can support finding related materials by searching development documents, code descriptions, and operation guidelines. Actual source code search may require separate configuration such as code indexing and permission management.
06. RAG Architecture for Your Environment
Not every company needs the same RAG configuration. The suitable architecture varies by data volume and format, user count, security requirements, and existing systems.
Local AI-Based RAG
Operate the AI model and search system on your own servers. Suitable for environments that must limit external data transmission or want to run AI on their own infrastructure.
External AI API-Integrated RAG
Perform document search on your own system and use an external AI API for answer generation. It reduces model operation burden, but you must review the scope of transmitted data and external service policies.
Hybrid RAG
Selectively use local and external AI models based on document sensitivity and work characteristics. Clearly defining each model’s role and data transmission policy is important.
Existing Business System-Integrated RAG
Integrate search with internal portals, business programs, databases, and more so employees can use knowledge AI within their existing work environment.
07. How do we manage RAG quality?
RAG is not complete just by connecting documents. You must continuously check whether search results are relevant to the question, whether the AI accurately reflects retrieved content, and whether users actually get the answers they need.
Search Quality Validation
Check whether relevant documents are retrieved based on real work questions, and improve missed or unnecessary results.
Answer Accuracy Validation
Compare against reference answers to review whether the AI’s answer matches facts and sufficiently reflects retrieved materials.
Source & Evidence Verification
Verify that the documents and source locations shown in the answer actually support the answer content.
Performance & Operation Validation
Review response time per question, concurrent request handling, document update time, and resource usage.
Continuous Improvement
Analyze user questions and search failure cases to improve document splitting, search settings, prompts, and model configuration.
Preparing validation criteria and test questions helps objectively evaluate whether the system fits real work, rather than just judging whether answers sound plausible.
08. Setup Process
Work & Data Analysis
Review the work you want to solve with AI, the types of documents you hold, their storage locations, and data access permissions.
Search & AI Architecture Design
Design document handling, embedding models, search engines, AI models, security policies, and integration scope.
Data Collection & Search Environment Setup
Clean documents and generate indexes, then connect the search system with AI answer generation.
Real Work Scenario Validation
Use representative questions and reference answers to validate search quality, answer accuracy, source provision, and response speed.
Operation & Enhancement
Establish document update and permission management systems, and improve search and answer quality based on usage results.
09. Frequently Asked Questions
RAG is a technology that uses retrieved document content in AI answers. Documents must be collected properly and appropriate search results provided; complex tables, images, and scanned documents may need separate processing.
Your company’s knowledge —
into a competitive advantage.
Your company’s documents accumulate work know-how, technology, experience, and information needed for decision-making. What matters is making this knowledge easy to find and use when you need it.
From internal document integration to intelligent search, AI Q&A, and local AI integration — we design and build a RAG system that fits your data and work environment.