start
  • đź‘‹Welcome
  • đź“–Introduction
  • đź’ˇUse Cases
  • đź§‘Personas
    • Film Production
    • Animation Studios
    • Game Developer
    • Industrial Design
    • Advertising
    • AI Image Generation / Text-to-Image
    • Speech-to-Text & Text-to-Speech
    • AI Video Enhancement & Processing
    • AI Object Detection & Image Analysis
    • Enterprise LLM API
    • Private Knowledge Base LLM (RAG - Retrieval-Augmented Generation)
    • Family Photographer
    • Indie Game Developer
    • Aspiring 3D Artist
    • Playstation Gamer
  • 🚀Get Started
    • Janction Node Operation Graphic Tutorial
  • đź”—Related Work
  • 🏗️Architecture
    • Actor Model
  • 🖥️Pooling
  • 🪙Token
  • ⚡Colocation of Idle Processor Computing Power
  • âś…Proof of Contribution
  • 🎮GPU Marketplace
    • Pricing strategy based on pvcg
  • âť“HELP FAQ
    • FAQ
      • How Janction Efficiently Stores AI/ML Models for Different Users?
      • Compared to traditional cloud GPU platforms, how does Janction's distributed idle GPU computing powe
      • How does Janction ensure the efficiency and quality of data annotation for various data types with d
      • How does Janction's execution layer handle the various AI subdomain functionalities?
      • How does Janction select and use different DAs?
      • Is Janction considering adopting the security guarantees provided by Restaking?
      • What is the current progress of Janction’s product technology?
      • How will Janction consider airdropping to the community?
  • 🛣️Roadmap
  • 📜Policy
    • Terms
Powered by GitBook
On this page
  1. Personas

Private Knowledge Base LLM (RAG - Retrieval-Augmented Generation)

PreviousEnterprise LLM APINextFamily Photographer

Last updated 2 months ago

“I’m Sarah, an AI knowledge engineer building secure enterprise LLMs. Janction gives me the GPU power to deploy real-time, private knowledge retrieval at scale.”

📖 I’m Sarah Williams, a 39-year-old AI knowledge engineer at InfoGuard Solutions in Washington, D.C. My job is to develop private, enterprise-grade LLMs that provide accurate, real-time answers from internal knowledge bases—while ensuring full compliance with GDPR, HIPAA, and SOC 2 regulations. Our clients can’t afford data leaks or reliance on public APIs, so we need secure, high-performance Retrieval-Augmented Generation (RAG) pipelines.

đź’» My problem?

Retrieving accurate answers from massive corporate document databases requires powerful GPUs. Even with A100 and H100 GPUs, running real-time retrieval and inference workloads is resource-intensive. Fine-tuning models like Llama 3, Mistral, and Falcon with company-specific data demands large-scale GPU clusters, and cloud-based hosting is too expensive and poses security risks. To scale efficiently while keeping data private, we need on-demand, enterprise-grade compute power.

🚀 That’s why I use Janction.

Janction’s scalable GPU pool allows me to fine-tune and deploy private RAG-based LLMs efficiently. Instead of waiting hours for model training or overpaying for cloud AI services, I can securely process massive document sets, optimize enterprise search, and deliver real-time insights—without sacrificing privacy or performance.

đź’ˇ What I love about Janction:

✅ Fast, private enterprise AI – Keeps all document processing on-premise for compliance.

✅ Real-time retrieval-augmented search – Enables instant, accurate responses from vast knowledge bases.

✅ Scalable GPU inference – Supports high-throughput, low-latency document queries.

✅ Affordable LLM hosting – Eliminates the high costs of OpenAI, AWS, or Azure-hosted models.

✅ Works with top vector databases – Seamlessly integrates with FAISS, ChromaDB, and Milvus.

🔍 Now, I can focus on delivering powerful, enterprise-grade AI search solutions. Thanks to Janction, my team deploys secure, real-time knowledge retrieval systems faster, at scale, and without compliance risks—transforming the way businesses access and use their data.

đź§‘