What is a RAG System?
RAG (Retrieval-Augmented Generation) is a next-gen AI architecture that combines:
- 🔍 Retrieval: Pulling relevant data from trusted sources (docs, database)
- 🧠 Generation: Using LLM (e.g., GPT, Claude, Gemini) to create accurate, context-aware answers
It’s ideal for enterprise use-cases where AI needs to generate content grounded in internal knowledge, not just public web data.
🧩 Why RAG + AI Agents Matter for Business
Traditional AI chatbots may hallucinate or provide outdated info.
RAG systems solve this by integrating:
- Internal documents
- Company policies
- Product manuals
- CRM or knowledge bases
This enables:
- ✅ More accurate answers
- ✅ Better user trust
- ✅ Real-time document grounding
- ✅ Secure enterprise use
🌏 Vietnam: A Growing Hub for RAG System Development
Vietnam’s AI ecosystem is evolving fast — and companies like NKKTech Global are now delivering RAG + AI Agent solutions at enterprise scale, for global clients.
Why Vietnam?
- 📉 Lower dev cost than US/EU
- 👨💻 Strong backend + AI engineering talent
- 💬 Language versatility (English + Japanese)
- 🌐 Familiarity with LangChain, LangGraph, LlamaIndex, Weaviate, and Pinecone
🏆 NKKTech Global’s RAG + AI Agent Capabilities
As a trusted AI company in Vietnam, NKKTech Global has deployed RAG solutions for internal search, customer service, and training systems.
⚙️ Technical Stack:
- LangChain / LangGraph for agent orchestration
- VectorDB: Weaviate, ChromaDB, or Pinecone
- LLMs: GPT-4o, Claude 3, Gemini 1.5
- Embedding: OpenAI, Cohere, or SentenceTransformer
- Frontend: React + Supabase (or Firebase)
🔧 Real-World Use Cases
1. 🎓 Internal Training Assistant
An AI agent that answers employee questions using company training materials (PDFs, video transcripts, manuals)
2. 📚 Smart Document Search
Upload thousands of docs → Ask anything → Get precise answers with source citations
3. 💬 RAG-Based Customer Support
Connects product manual + support tickets → Delivers consistent, always-on support via AI agent
4. 🧾 Financial Report AI Assistant
Staff asks AI to summarize or interpret key parts of complex financial docs instantly
🔍 How the System Works (Simplified)
mermaidSao chépChỉnh sửagraph TD
A[User Question] --> B[Embed & Retrieve Relevant Chunks]
B --> C[Context Injection to LLM]
C --> D[LLM Generates Answer]
D --> E[AI Agent Responds with Cited Sources]
Optional:
- Store user questions + answers in Supabase
- Use long-term memory or role-based access
📊 Why Choose NKKTech Global for RAG Systems?
| Feature | NKKTech Global |
|---|---|
| Custom RAG Development | ✅ End-to-end from scratch |
| Multi-language Support | ✅ English, Japanese, Vietnamese |
| Cost Efficiency | ✅ 30–50% cheaper than US teams |
| LLM Agnostic (GPT, Gemini…) | ✅ Supports multiple models |
| Integration with Client Infra | ✅ Flexible API & DB support |
🧠 Bonus: Combine with Voice AI
NKKTech also integrates voice input + RAG for:
- Voice Q&A agents
- Field assistant bots
- Medical support systems
💡 “Just speak – and your agent fetches the answer in seconds.”
📞 Ready to Build Your Enterprise AI Agent?
NKKTech Global provides:
- 🎯 Fast prototyping in 7–14 days
- 🔐 Secure architecture for sensitive data
- 🔁 Long-term support & updates
🌐 Website: https://nkk.com.vn
📧 Email: contact@nkk.com.vn
