ReimeiTech builds AI-powered knowledge systems that connect to your documents, websites, databases, PDFs, SOPs, FAQs, and internal resources — so users get accurate, source-backed answers.
Not generic AI memory. Your knowledge, retrieved and answered with citations — searchable from chat, embedded widgets, internal portals, or Slack.
A RAG knowledge base is an AI system that retrieves relevant information from your own content before generating an answer.
Instead of relying only on the AI model's general knowledge, the system searches your company documents, policies, files, databases, or website content — and uses that information to produce more accurate, business-specific responses.
Confidently wrong on company-specific questions.
Grounded in your actual content — not the model's training set.
A RAG knowledge base gives your team one place to ask questions and receive answers grounded in your actual business content.
"Where is that document again?"— the most expensive question in your business
Help employees search company policies, SOPs, training materials, and internal guides.
Answer customer questions from your product docs, FAQs, help center, and support content.
Let users upload or search documents and ask questions directly from their content.
AI assistants trained on your public site, blog, documentation, service and product pages.
Secure portals where staff, clients, or partners access AI search with permissions and source controls.
Not "AI chat." A complete knowledge product — with the ingestion, retrieval, security, admin, and observability your team needs to rely on it.
A strong RAG system needs clean content ingestion, chunking, embedding, vector search, retrieval logic, prompt design, answer generation, source citation, and security controls.
Employees ask from company policies, SOPs, training documents, and internal guides.
Customers and support agents ask from help articles, product manuals, FAQs, and support content.
Sales teams ask from product documents, pricing rules, case studies, and proposal materials.
Teams search compliance documents, policies, procedures, and internal standards faster.
Engineering and support teams search API docs, product docs, troubleshooting guides, and technical manuals.
We organize your existing knowledge, prepare it for AI search, and connect it to an assistant your team or customers can actually use.
A RAG system should not generate confident-sounding answers from thin air. We design knowledge assistants to show where answers came from, which documents were used, and when the system needs human review.
"The goal is not just to answer fast. The goal is to answer from the right source."
Not every user should access every document. We build RAG systems with permissions, user roles, access controls, and audit logs — essential for healthcare, fintech, agencies, and any serious business workload.
Full control over content, users, permissions, and the AI's answer quality.
Not just a chat widget. A complete admin product for the team responsible for content quality.
Every RAG project ships as a working knowledge product — not a demo prompt — with the controls, integrations, and documentation your team needs to rely on it.
We choose the architecture based on your content volume, privacy needs, user roles, update frequency, and business workflow.
Not religious about any tool — pragmatic about all of them.
Employees ask. The assistant retrieves the relevant policy section, summarizes it, and cites the source — with admin tools to keep answers accurate.
Upload company documents
System indexes content
Employee asks a question
AI retrieves relevant policy sections
AI answers with source citations
Employee gives feedback
Admin improves knowledge base
Turn product docs, onboarding materials, and internal knowledge into a searchable AI assistant.
Help teams search client information, SOPs, service documentation, and reporting guides.
Support staff knowledge access, policy search, intake docs, and secure internal guidance.
Search compliance documents, reporting rules, product knowledge, and internal procedures.
Centralize FAQs, service details, customer policies, and employee training materials.
Six steps, every engagement — each ending with an artifact your team can review.
We review your documents, websites, databases, and knowledge sources.
We decide which sources to include, exclude, restrict, or update regularly.
We design ingestion, chunking, embeddings, retrieval, citations, permissions, UI.
We build the backend, search logic, AI answer layer, frontend, and admin tools.
We test real questions, answer quality, source accuracy, permissions, edge cases.
We launch, monitor usage, improve weak answers, and update knowledge over time.
End-to-end automations with retries, observability, and human review.
Tool-using agents that complete multi-step business tasks.
Extract structured data from messy PDFs, invoices, and contracts.
First-response automation that actually deflects tickets.
Narrative reports generated from your raw data.
Cross-system automations beyond what LLMs alone can do.
Role-specific copilots embedded in your team's tools.
Tell us where your company knowledge lives today. We'll help you design a secure RAG knowledge base that makes your documents, policies, FAQs, and internal resources easier to search and use.
